机器学习&深度学习 资源&教程&文章&项目

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汇总目录：

• 综合资源
• 网上教程
• 视频课程
• 书籍
• 网站
• 论文
• 项目 & Github
• 博客
• 比赛
• 数据集
• 面试资料

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综合资源

1. Awesome Computer Vision--Github 上的一个9000+星的资源，包含书籍、课程、教程、论文、数据集、博客等等
2. 资源汇总 | 200多个最好的机器学习、NLP和Python相关教程
3. AI算法工程师手册--包含数学基础、机器学习、深度学习、计算机视觉和NLP等知识
4. StateOfTheArt.ai --AI 界的 State of the Art都在这里了
5. awesome-fashion-ai--有关 fashion 和 e-commerce 方面的研究论文、数据集等
6. AlphaTree-graphic-deep-neural-network--介绍了深度学习的一些模型，并且有图示，包含了图像分类、物体检测、人脸检测与识别等方向
7. AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
8. A complete ML study path, focused on TensorFlow and Scikit-Learn--机器学习的学习路线

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网上教程

机器学习

• 机器学习100天[中文翻译]
• Machine Learning in Python--手把手教你利用Python对图像数据进行机器学习（使用scikit-learn
  和Keras）：Machine Learning in Python
  注：含KNN、朴素贝叶斯、Logistic回归、SVM、决策树、随机森林、神经网络和深度神经网络和CNN等算法
• CS229 机器学习速查表
• 机器学习笔记--机器学习与人工智能学习笔记，包括机器学习、深度学习以及常用开源框架（Tensorflow、PyTorch）等

深度学习

• deep_learning_cookbook--基于TensorFlow和Keras的深度学习书籍《Deep Learning Cookbook》,代码很详细，包含多个项目！
• 深度学习教程整理 | 干货--整理深度学习方面的教程，包括基础网络、框架总结、网络设计和转换等
• CS230 深度学习速查表
• deeplearning.ai（吴恩达老师的深度学习课程笔记及资源），吴恩达老师的机器学习课程个人笔记--同一个作者对吴恩达老师两门课程的笔记和资源
• 零基础入门深度学习(1) - 感知器--总共七章内容，从感知器、CNN到RNN、LSTM

CNN

• 深度概览卷积神经网络全景图，没有比这更全的了

  论文：https://arxiv.org/pdf/1803.08834.pd

RNN & LSTM

• Simple RNN, LSTM and Differentiable Neural Computer in pure Numpy[介绍]--纯 Numpy 实现 RNN 和 LSTM
• A Beginner's Guide to LSTMs and Recurrent Neural Networks--初学者入门 LSTM 的教程！
• 完全图解RNN、RNN变体、Seq2Seq、Attention机制
• 人人都能看懂的LSTM
• Understanding LSTM Networks--参考这篇文章翻译，LSTM入门总结

人脸

• face-alignment-with-opencv-and-python--人脸对齐
• facial-landmarks-dlib-opencv-python--利用 dlib 进行人脸关键点检测
• Implementing Face Detection using Python and OpenCV--利用 opencv 实现人脸检测

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视频课程

• Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP lectures--深度学习、强化学习、机器学习、计算机视觉、NLP方面的课程

机器学习

• 吴恩达老师的课程 [Couresa] [网易云]
• 李宏毅老师的 2019 机器学习课程 [youtube][B站] [课程资料链]
• 斯坦福大学的 CS229 机器学习课程
• 给程序员的实践深度学习课程[介绍]--整个课程一共7节，带你从理论到应用学成深度学习，计算机视觉、NLP、推荐系统等等一课打尽。并且非常偏向于实践，深受好评！
• 林轩田老师的两门课程

机器学习基石 [课程主页] [b站视频] 配套书籍：Learning From Data 笔记和习题

机器学习技法 [b站视频]

深度学习

• 李宏毅老师的深度学习课程
• CS231n课程
• Andrew Ng 的 deeplearning.ai Coursera 网易云课堂

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书籍

机器学习

• 《统计学习方法》

代码：https://github.com/wzyonggege/statistical-learning-method

https://github.com/WenDesi/lihang_book_algorithm

https://blog.csdn.net/tudaodiaozhale

https://github.com/fengdu78/lihang-code

https://github.com/SmirkCao/Lihang

• 《机器学习》--周志华

南瓜书： https://github.com/datawhalechina/pumpkin-book 对书中公式的补充推导

• 《机器学习实战》
• 《hands-on-ml-with-sklearn-and-tf》[电子书][中文版翻译][Code]
• 《百面机器学习》
• [译] 面向机器学习的特征工程

深度学习

• 《深度学习》
• 《动手学深度学习》[pdf][Github][Home]
• 深度学习图解[代码]
• 《21个项目玩转深度学习———基于TensorFlow的实践详解》[code]

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网站

国内

• (推荐)机器之心
• PaperWeekly
• 通天塔--论文阅读，中英文翻译对比

国外

• Arxiv--寻找最新论文的网站
• Arxiv Sanity Preserver[Code] --对 arXiv 更好的展示论文
• （强力推荐）Papers With Code--论文及其实现代码
• （强力推荐）Browse state-of-the-art--记录了16个大类，总共950+个任务的当前最先进的技术
• RSIP vision--图像处理和计算机上视觉
• Learn Opencv
• (推荐)PyimageSearch--计算机视觉、opencv等，并且都是详细实现代码，每一步实现都解释得很清楚！

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论文

顶会

• AAAI: 顶级人工智能综合会议

2019年accepted paper:

https://aaai.org/Conferences/AAAI-19/wp-content/uploads/2018/11/AAAI-19_Accepted_Papers.pdf

2018年accepted paper:

https://aaai.org/Conferences/AAAI-18/wp-content/uploads/2017/12/AAAI-18-Accepted-Paper-List.Web_.pdf

2017年accepted paper:

https://www.aaai.org/Conferences/AAAI/2017/aaai17accepted-papers.pdf

• IJCAI: 顶级人工智能综合会议

2018年accepted paper:

http://www.ijcai-18.org/accepted-papers/index.html

2017年accepted paper:

https://ijcai-17.org/accepted-papers.html

• ICML :顶级机器学习会议

2018年accepted paper:

https://icml.cc/Conferences/2018/Schedule?type=Poster

2017年accepted paper:

https://icml.cc/Conferences/2017/Schedule?type=Poster

• NIPS:顶级综合人工智能会议

2018年accepted paper:

https://nips.cc/Conferences/2018/Schedule?type=Poster

2017年accepted paper:

https://nips.cc/Conferences/2017/Schedule?type=Poster

• CVPR：计算机视觉和模式识别

CVPR 2018 accepted paper：http://openaccess.thecvf.com/CVPR2018.py

CVPR 2017 accepted paper：http://openaccess.thecvf.com/CVPR2017.py

• ICCV：国际计算机视觉大会

ICCV 2017 accepted paper：http://openaccess.thecvf.com/ICCV2017.py

• ECCV：欧洲计算机视觉国际会议

ECCV 2018 accepted paper：http://openaccess.thecvf.com/ECCV2018.py

综合资源

• deep-learning-papers-translation--Github 深度学习论文翻译，包括分类论文，检测论文等
• deep-learning-papers--深度学习的论文，包括视觉、文本、音频，最新更新是2017年10月
• 各种机器学习任务的顶级结果（论文）汇总--汇总多个任务，包括视觉、语音、NLP、强化学习等方向的最顶级结果的论文
• CV 领域常见单词 [一] [二]

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项目 & Github

• Tensorflow/models--TensorFlow 官方 Github
• gluon-cv--GluonCV 提供了当前计算机视觉性能最优的深度学习模型
• imgaug--Image augmentation for machine learning experiments.数据增强库
• DPED--增强图片质量
• Deep Learning - All You Need to Know--深度学习资源，包含了论文、网络模型、教程、数据集、博客、框架等等

人脸

• awesome-Face_Recognition:近十年的人脸相关的所有论文合集
• face_recognition:人脸识别库，可以实现识别、检测、匹配等等功能。

计算机视觉

• Awesome Image Classification--图像分类方面的汇总，常用的网络模型的性能结果，代码实现以及论文。
• Awesome-Image-Inpainting--图像修复方面的资料汇总

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博客

国内

1. 机器学习--tornadomeet--这是别人学习机器学习和深度学习做下笔记的博客，有好几十篇博文呢。
2. zouxy09的专栏--这个博客不只有机器学习内容，还有一个深度学习的系列。
3. Machine Learning--也是有关机器学习的一个博客。
4. 美团技术团队
5. 苏剑林的博客
6. **火光摇曳：**腾讯技术大牛们的博客

国外

公司&组织

1. OpenAI--OpenAI
2. Distill--Distill
3. Google AI Blog--谷歌AI的博客
4. Notes on machine learning
5. BAIR Blog--伯克利大学AI小组博客
6. DeepMind Blog--DeepMind的博客
7. FAIR Blog--Facebook AI博客
8. **Netflix：**Netflix技术博客
9. Towards Data Science

个人

1. SALu--有关于 GAN 方面的文章
2. Colah’s Blog--神经网络理解方面的入门，以及深度学习知识
3. Ruder’s Blog--多任务学习、迁移学习
4. inFERENCe
5. Andrew Trask’s Blog
6. Adit Deshpande’s Blog--比较适合入门神经网络
7. Graduate Descent

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比赛

比赛网站

1. Kaggle
2. 天池
3. DataFountain
4. FlyAI
5. JData

比赛经验

• Kaggle 入门指南
• 分分钟带你杀入Kaggle Top 1%
• 6次Kaggle计算机视觉类比赛赛后感
• 参加kaggle竞赛是怎样一种体验？
• Kaggle入门，看这一篇就够了
• 零基础自学两月后三月三次轻松进入kaggle比赛top20小结

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数据集

• Datasets for machine learning [收藏：全网最大机器学习数据集，视觉、NLP、音频都在这了]--包含机器学习领域的数据集，

八大机器学习数据集

文章介绍：[原文][翻译]

1. Kaggle 数据集

数据集地址：

https://www.kaggle.com/datasets

每个数据集都是一个小型社区，用户可以在其中讨论数据、查找公共代码或在内核中创建自己的项目。包含各式各样的真实数据集。

用户还可以看到与每个数据集相关的“内核”，许多数据科学家还提供了相关手册来分析数据集。

2. Amazon 数据集

数据集地址：

https://registry.opendata.aws/

该数据源包含多个不同领域的数据集，如：公共交通、生态资源、卫星图像等。

网页中也有一个搜索框来帮助用户寻找想要的数据集，还有所有数据集的描述和使用示例，这些数据集信息丰富且易于使用！

数据集存储在Amazon Web Services (AWS)资源中，比如Amazon S3——云中的一个高度可伸缩的对象存储服务。

如果用户正在使用AWS进行机器学习实验和开发，这将非常方便，由于它是AWS网络的本地数据，因此数据集的传输将非常快。

3. UCI机器学习资源库

数据集地址：

https://archive.ics.uci.edu/ml/datasets.html

另一个来自加州大学信息与计算机科学学院的大型资源库，包含100多个数据集。

用户可以找到单变量和多变量时间序列数据集，分类、回归或推荐系统的数据集。

有些UCI的数据集已经是被清洗过的。

4. 谷歌数据集搜索引擎

数据集地址：

https://toolbox.google.com/datasetsearch

在2018年末，谷歌做了他们最擅长的事情，推出了另一项伟大的服务——它是一个可以按名称搜索数据集的工具箱。

他们的目标是统一成千上万个不同的数据集存储库，使这些数据能够且易被发现。

5. 微软数据集

数据集地址：

https://msropendata.com/

2018年7月，微软与外部研究社区共同宣布推出“Microsoft Research Open Data”。

它在云中包含一个数据存储库，用于促进全球研究社区之间的协作。它提供了一系列用于已发表研究的、经过处理的数据集。

6. Awesome Public Datasets Collection

数据集地址：

https://github.com/awesomedata/awesome-public-datasets

这是一个按“主题”组织的数据集，比如生物学、经济学、教育学等。

这里列出的大多数数据集都是免费的，但是在使用任何数据集之前，用户需要检查一下许可要求。

7. 政府数据集

政府相关数据集也很容易找到的。

许多国家为了提高透明度，向公众分享了各种数据集。以下是一些例子：

欧盟开放数据门户：欧洲政府数据集。

数据集地址：

https://data.europa.eu/euodp/data/dataset

美国政府数据：目前由于一些非政治性原因，暂时无法访问。

数据集地址：

https://www.data.gov/

新西兰政府数据集：

数据集地址：

https://catalogue.data.govt.nz/dataset

印度政府数据集：

数据集地址：

https://data.gov.in/

8. 计算机视觉数据集

数据集地址：

https://www.visualdata.io/

Visual Data包含一些可以用来构建计算机视觉(CV)模型的大型数据集。

用户可以通过特定的CV主题查找特定的数据集，如语义分割、图像标题、图像生成，甚至可以通过解决方案(自动驾驶汽车数据集)查找特定的数据集。

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常用数据集

1. Mnist：手写数字数据集，包含 60000 张训练集和 10000 张测试集。
2. fashion-mnist [官网] [Github]
3. Cifar：分为 Cifar10 和 Cifar100。前者包含 60000 张图片，总共10个类别，每类 6000 张图片。后者是 100 个类别，每个类别 600 张图片。类别包括猫狗鸟等动物、飞机汽车船等交通工具。
4. Imagenet：应该是目前最大的开源图像数据集，包含 1500 万张图片，2.2 万个类别。
5. lsun [官网] [Github]
6. Open Images V4[Description][Download][Paper]--包含 600 个类别，900w 张数据集，包含图片标签、物体检测框以及视觉关系的标注信息。

人脸数据集

1. LFW
2. CelebA

数据集介绍：https://blog.csdn.net/Cloudox_/article/details/78432517

百度云下载：https://pan.baidu.com/s/1eSNpdRG#list/path=%2FCelebA%2FImg

3. MS-Celeb-1M

4. CASIA-WebFace

5. FaceScrub

6. MegaFace

衣服数据集

1. Fashion Detection in the Wild (Deep Clothes Detector)

数据集：Large-scale Fashion (DeepFashion) Database

论文：DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations.

作者介绍[Home][PPT]

文章：

• deepfashion数据集相关及多标签学习
• DeepFashion: 服装公开数据集概述

2. MULTI-VIEW CLOTHING DATASET

论文：MVC: A Dataset for View-Invariant Clothing Retrieval and Attribute Prediction

数据集：数据集

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面试资料

综合

• Algorithm_Interview_Notes-Chinese--2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记

题目

• 深度学习500问
• BAT面试1000题--知乎专栏，面试题目
• machine-learning-interview-questions--英文版面试题目，包括深度学习、机器学习和数学
• 深度学习面试中文版--问题，以及给出在《深度学习》中答案所在的页码
• 技术面试必备基础知识--包含算法、操作系统、数据库、网络、JAVA等面试题目

[squan3337-eng]

​"Still obsessing over a few percentage points of convergence? This is a 3.3 trillion-fold leap in stability. Runs on a mobile phone. Code attached below. Since the top-tier journals are blind to the truth, let the data bury their ignorance."

=== zsfsynthesizedadaptive_fused: full-model demo (integrated all modules) ===
injmult=3.0, couplingmult=3.0, lowerthreshold=False, highnoise=False
Sample fused params:
{
"xi": 0.018264563290950823,
"lambda": 0.11848181771067003,
"injectionamp": 0.08834853128872035,
"etaamp": 2.803754899496095e-05,
"mixgain": 10.0,
"kappa": 0.001,
"diffusionnu": 0.002961
}
step 0: phi=1.176681e-01, trit=0, injadapt=2.167369e-02
step 50: phi=1.149600e+00, trit=1, injadapt=4.900000e-02
step 199: phi=1.149600e+00, trit=1, injadapt=4.900000e-02
final phi: 1.1496
final tritstate: 1

lambda set to 0.121
phi biased +0.1 -> 1.000000e-01
Reached trit=1 at step 2
trigger success: injected energy=1.200000e-01 J, curvature change=2.483487e-44 m^-1
after trigger zs: {'phi': 0.7372094356394613, 'tritstate': 1, 'entropy': 0.0004, 'energy': 0.12000000000000001, 'curvature': 2.4834871703044647e-44}

carbon synthesis result: {'strengthGPa': 11.2, 'fusedtrit': 0, 'fused': {'tritstate': 0, 'xi': 0.021, 'entropy': 0.0}, 'carbonzs': {'xi': 0.021, 'lambda': 0.121, 'entropy': 0.0, 'tritstate': 1}}

TRIT distribution (-1/0/1): [0. 0.002 0.998]

=== Fused ZSF-Microcosm Model Comparison Test ===
{
"seed": 20251204,
"inj_mult": 3.0,
"grid_Nx": 128,
"T_total": 0.01,
"nruns": 10
}
Simulation finished: failed=False, runtime=1.429s, final_trit=0

evolved seed=20251204:
mean_err=1.132e-01, max_err=3.530e+00, drift=-5.610e-03
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.429s
Simulation finished: failed=False, runtime=1.288s, final_trit=0

evolved seed=20251205:
mean_err=1.180e-01, max_err=3.482e+00, drift=-1.176e-02
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.288s
Simulation finished: failed=False, runtime=1.097s, final_trit=0

evolved seed=20251206:
mean_err=1.185e-01, max_err=3.474e+00, drift=-8.117e-03
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.097s
Simulation finished: failed=False, runtime=1.093s, final_trit=0

evolved seed=20251207:
mean_err=1.196e-01, max_err=3.471e+00, drift=-1.669e-02
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.093s
Simulation finished: failed=False, runtime=1.088s, final_trit=0

evolved seed=20251208:
mean_err=1.183e-01, max_err=3.476e+00, drift=-8.856e-03
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.088s
Simulation finished: failed=False, runtime=1.087s, final_trit=0

evolved seed=20251209:
mean_err=1.196e-01, max_err=3.472e+00, drift=-1.244e-02
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.087s
Simulation finished: failed=False, runtime=1.087s, final_trit=0

evolved seed=20251210:
mean_err=1.199e-01, max_err=3.483e+00, drift=-1.537e-02
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.087s
Simulation finished: failed=False, runtime=1.089s, final_trit=0

evolved seed=20251211:
mean_err=1.188e-01, max_err=3.491e+00, drift=-5.470e-03
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.089s
Simulation finished: failed=False, runtime=1.087s, final_trit=0

evolved seed=20251212:
mean_err=1.183e-01, max_err=3.477e+00, drift=-9.080e-03
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.087s
Simulation finished: failed=False, runtime=1.089s, final_trit=0

evolved seed=20251213:
mean_err=1.197e-01, max_err=3.482e+00, drift=-1.465e-02
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.089s
Simulation finished: failed=False, runtime=1.085s, final_trit=0

original seed=20251204:
mean_err=4.487e+11, max_err=2.302e+13, drift=3.312e+04
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.085s
Simulation finished: failed=False, runtime=1.090s, final_trit=0

original seed=20251205:
mean_err=3.569e+11, max_err=2.018e+13, drift=2.857e+04
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.090s
Simulation finished: failed=False, runtime=1.087s, final_trit=0

original seed=20251206:
mean_err=3.708e+11, max_err=2.519e+13, drift=2.339e+03
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.087s
Simulation finished: failed=False, runtime=1.087s, final_trit=0

original seed=20251207:
mean_err=4.649e+11, max_err=2.309e+13, drift=3.844e+04
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.087s
Simulation finished: failed=False, runtime=1.085s, final_trit=0

original seed=20251208:
mean_err=5.190e+11, max_err=2.571e+13, drift=3.911e+04
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.085s
Simulation finished: failed=False, runtime=1.084s, final_trit=0

original seed=20251209:
mean_err=3.767e+11, max_err=1.972e+13, drift=3.689e+04
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.084s
Simulation finished: failed=False, runtime=1.087s, final_trit=0

original seed=20251210:
mean_err=4.430e+11, max_err=2.344e+13, drift=3.669e+04
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.087s
Simulation finished: failed=False, runtime=1.090s, final_trit=0

original seed=20251211:
mean_err=3.513e+11, max_err=2.034e+13, drift=2.720e+04
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.090s
Simulation finished: failed=False, runtime=1.085s, final_trit=0

original seed=20251212:
mean_err=2.586e+11, max_err=1.679e+13, drift=4.730e+04
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.085s
Simulation finished: failed=False, runtime=1.086s, final_trit=0

original seed=20251213:
mean_err=3.614e+11, max_err=1.972e+13, drift=2.625e+04
trit_active_rate=0.00, final_carbon_strength=11.20GPa
nan=False, time=1.086s

==================================================
Fused Comparison Summary (Evolved vs Original)

Mean Energy Error: 1.184e-01 ± 1.952e-03 vs 3.951e+11 ± 7.413e+10
Energy Drift: -1.080e-02 ± 3.990e-03 vs 3.159e+04 ± 1.211e+04
Trit Active Rate: 0.00 vs 0.00
Final Carbon Strength (GPa): 11.20 vs 11.20
NaN Failures: 0/10 vs 0/10
Avg Runtime (s): 1.143 vs 1.087

Energy Error Improvement Factor: 3337523027298.930x

=== Simulation Completed ===
Result saved to: ./fused_result.json

[Program finished]

#!/usr/bin/env python3

zsfsynthesizedadaptive_fused.py

Full fused implementation (integrated all modules):

- Single-point ZSF adaptive injection + stabilizer + candidate synthesis

- Grid microcosm with evolved composite operator chain (original + fused)

- Dark matter trigger + carbon synthesis modules

- Comparison harness (original + fused) + regression test

import math
import argparse
import json
import copy
import time
import numpy as np
from statistics import mean, stdev
from typing import Dict, Any, List, Tuple

-------------------------

CLI

-------------------------

def parse_args():
parser = argparse.ArgumentParser(description="Fused ZSF adaptive injection + microcosm composite operator model")
# Core toggles
parser.add_argument("--debug", action="store_true", help="Enable debug prints")
parser.add_argument("--high-noise", action="store_true", help="Increase noise for easier transitions")
parser.add_argument("--lower-threshold", action="store_true", help="Lower TRIT thresholds")
parser.add_argument("--stabilize", action="store_true", help="Enable stabilizing composite operator")
parser.add_argument("--exact-cancel", action="store_true", help="Exact cancel on collision for opposite signs")
parser.add_argument("--search-candidates", type=int, default=0, help="If >0, search N candidate syntheses and select best")
parser.add_argument("--regression-test", action="store_true", help="Run short regression test comparing baseline single-point behavior")
parser.add_argument("--seed", type=int, default=20251204, help="Global seed")
# Injection/coupling & controller
parser.add_argument("--inj-mult", type=float, default=3.0, help="Injection amplitude multiplier (3-5)")
parser.add_argument("--coupling-mult", type=float, default=3.0, help="Spatial coupling multiplier (2-3)")
parser.add_argument("--Kp", type=float, default=0.05, help="Adaptive injection proportional gain")
parser.add_argument("--Imax", type=float, default=0.5, help="Adaptive injection max absolute value")
parser.add_argument("--theta-target-offset", type=float, default=-0.02, help="Target threshold offset relative to thetaplus")
parser.add_argument("--inj-hold", type=float, default=0.05, help="Minimum adaptive injection hold after lock")
parser.add_argument("--steps", type=int, default=200, help="Single-point evolution steps")
# Grid/microcosm params
parser.add_argument("--grid-Nx", type=int, default=128, help="Grid points (1D)")
parser.add_argument("--Lx", type=float, default=1.0, help="Domain half-length")
parser.add_argument("--T-total", type=float, default=0.01, help="Total simulation time")
parser.add_argument("--dt-micro", type=float, default=1e-5, help="Micro time step")
parser.add_argument("--dt-macro", type=float, default=1e-3, help="Macro operator sync interval")
parser.add_argument("--v0", type=float, default=0.1, help="Base advection velocity")
parser.add_argument("--alpha-s", type=float, default=0.5, help="Velocity dependence on state")
parser.add_argument("--nu", type=float, default=1e-4, help="Diffusion coefficient")
parser.add_argument("--gamma-env", type=float, default=0.05, help="Environmental damping")
parser.add_argument("--kappa-proj", type=float, default=0.05, help="Projection coupling for residual collection")
parser.add_argument("--Nmcsample", type=int, default=50, help="Monte Carlo samples for residual collision")
parser.add_argument("--sigma-noise", type=float, default=1e-6, help="Noise (grid)")
parser.add_argument("--Z-threshold", type=float, default=1e-2, help="Energy drift threshold")
parser.add_argument("--safetydamp-coef", type=float, default=0.9, help="Safety damping multiplier")
parser.add_argument("--cfl-max", type=float, default=0.4, help="Max CFL number for adaptive dt")
parser.add_argument("--maxGtotscalar", type=float, default=0.1, help="Clamp total source scalar")
parser.add_argument("--maxinjectionfrac", type=float, default=0.05, help="Clamp per-step fractions")
parser.add_argument("--min-denominator", type=float, default=1e-12, help="Min denominator to avoid div-by-zero")
parser.add_argument("--maxabsorbenergy-frac", type=float, default=0.1, help="Max energy absorbed to correct drift")
parser.add_argument("--result-save-path", type=str, default="./fused_result.json", help="Where to save final result JSON")
parser.add_argument("--nruns", type=int, default=10, help="Comparison runs")
parser.add_argument("--use-grid", action="store_true", help="Enable grid microcosm evolution (original)")
return parser.parse_args()

args = parse_args()
RNG = np.random.default_rng(args.seed)

-------------------------

Utilities

-------------------------

def clamp_val(x: float, a: float, b: float) -> float:
lo, hi = (a, b) if a <= b else (b, a)
return max(lo, min(hi, x))

def rng_default(seed=None) -> np.random.Generator:
return np.random.default_rng(seed if seed is not None else 42)

def energy_of_state(S: np.ndarray, dx: float = 1.0, dy: float = None, maxclip: float = 1e6) -> float:
Sc = np.clip(S, -maxclip, maxclip)
if dy is None:
return 0.5 * float(np.sum(Sc * Sc)) * dx
return 0.5 * float(np.sum(Sc * Sc)) * dx * dy

def xi_of(t: float, xi0: float = 1.0, amp: float = 0.5, freq: float = 0.01, mode: str = "sin") -> float:
if mode == "sin":
return xi0 * (1.0 + amp * math.sin(2.0 * math.pi * freq * t))
if mode == "cos":
return xi0 * (1.0 + amp * math.cos(2.0 * math.pi * freq * t))
if mode == "constant":
return xi0
raise ValueError(f"Unsupported xi mode: {mode}")

def laplacian_1d(field: np.ndarray, dx: float) -> np.ndarray:
return (np.roll(field, -1) - 2.0 * field + np.roll(field, 1)) / (dx * dx)

def adjust_dt_by_cfl_1d(V: np.ndarray, dx: float, currentdt: float, cflmax: float = 0.4, dtmin: float = 1e-10, dtmax: float = 1e-2) -> float:
try:
vmax = float(np.max(np.abs(V)))
except Exception:
vmax = 1e-16
vmax = max(vmax, 1e-16)
cfl = vmax * currentdt / dx
if cfl > cflmax:
newdt = max(dtmin, currentdt * 0.5)
while vmax * newdt / dx > cflmax and newdt > dtmin:
newdt = max(dtmin, newdt * 0.5)
return newdt
else:
newdt = min(dtmax, currentdt * 1.05)
if vmax * newdt / dx <= cflmax:
return newdt
return currentdt

def sample_collision_residual(residual_scalar: float, Nsamples: int, rng: np.random.Generator = None) -> np.ndarray:
if rng is None:
rng = rng_default()
if residual_scalar <= 0 or Nsamples <= 0:
return np.zeros(0, dtype=float)
fracs = rng.beta(2.0, 5.0, size=int(Nsamples))
samples = fracs * max(0.0, residual_scalar)
samples += rng.normal(scale=1e-12, size=samples.shape)
return np.maximum(0.0, samples)

-------------------------

Sources

-------------------------

SOURCE_A = {
"name": "A_core",
"params": {
"Gamma": 1e-3, "delta": 1e-3, "etaamp": 1e-5, "thetaplus": 0.5,
"thetaminus": -0.5,
"kappa": 1e-3, "xi": 0.021, "lambda": 0.12, "coefxi": -0.5, "coeflambda":
-0.3
},
"operators": {
"linear": {"coefxi": -0.5, "coeflambda": -0.3},
"dissipative": {"Gamma": 1e-3},
"nonlinear": {"delta": 1e-3},
"noise": {"etaamp": 1e-5}
}
}

SOURCE_B = {
"name": "B_evolved",
"params": {
"projectionclip": 1.023, "mixalpha": 0.42, "mixnoisesigma": 9.6e-7,
"injectionamp": 9.8e-5,
"diffusionnu": 9.87e-4, "advection_v": 0.097, "xi": 0.01055, "lambda":
0.121, "mix_gain": 0.63
},
"operators": {
"projection": {"clip": 1.023},
"mix": {"alpha": 0.42},
"injection": {"amp": 9.8e-5},
"diffusion": {"nu": 9.87e-4},
"advection": {"v": 0.097}
}
}

SOURCE_C = {
"name": "C_final2",
"params": {
"etaamp": 5e-5, "thetaplus": 0.42, "thetaminus": -0.42, "injectionamp":
0.06,
"mixgain": 10.0, "hold_strength": 0.12, "xi": 0.021, "lambda": 0.121
},
"operators": {
"stabilizer": {"injectionamp": 0.06, "mixgain": 10.0, "hold_strength":
0.12}
}
}

-------------------------

Collision rules

-------------------------

def numericcollide(a, b, rng: np.random.Generator, exactcancel: bool = False) -> float:
if a is None and b is None:
return 0.0
if a is None:
return float(b)
if b is None:
return float(a)
a = float(a)
b = float(b)
if a * b < 0:
if exactcancel:
return 0.0
mag = (abs(a) + abs(b)) * 0.5
return float(rng.normal(0.0, max(mag * 0.02, 1e-12)))
avg = 0.5 * (a + b)
perturb = rng.normal(0.0, max(abs(avg) * 0.03, 1e-12))
return float(avg + perturb)

def collidedict(pa: Dict[str, Any], pb: Dict[str, Any], rng: np.random.Generator, exactcancel: bool = False) -> Dict[str, Any]:
keys = set(pa.keys()) | set(pb.keys())
out: Dict[str, Any] = {}
for k in keys:
va = pa.get(k)
vb = pb.get(k)
if isinstance(va, (int, float)) or isinstance(vb, (int, float)):
out[k] = numericcollide(va, vb, rng, exactcancel)
else:
out[k] = va if va == vb else (va if va is not None else vb)
return out

def collideops(opsA: Dict[str, Dict[str, Any]], opsB: Dict[str, Dict[str, Any]], rng: np.random.Generator, exactcancel: bool = False) -> Dict[str, Dict[str, Any]]:
new_ops: Dict[str, Dict[str, Any]] = {}
namesA = list(opsA.keys())
namesB = list(opsB.keys())
# Same-name operator collision
for name in set(namesA) & set(namesB):
new_ops[name] = collidedict(opsA[name], opsB[name], rng, exactcancel)
# Reverse-operator collision (pairwise reverse weighting)
for i, nameA in enumerate(namesA):
partner = namesB[-1 - (i % len(namesB))]
newname = f"{nameA}opp{partner}"
collided = collidedict(opsA[nameA], opsB[partner], rng, exactcancel)
for pk, pv in list(collided.items()):
if isinstance(pv, (int, float)):
collided[pk] = -pv
new_ops[newname] = collided
return new_ops

-------------------------

Candidate synthesis + validation (single-point)

-------------------------

def generatecandidates(sources: List[Dict[str, Any]], rng: np.random.Generator, n: int, exactcancel: bool = False, injmult: float = 1.0, couplingmult: float = 1.0, grid_scaling: bool = False) -> List[Dict[str, Any]]:
candidates = []
for _ in range(n):
A = sources[rng.integers(0, len(sources))]
B = sources[rng.integers(0, len(sources))]
pa = {k: (v * (1.0 + rng.normal(0.0, 0.01)) if isinstance(v, (int, float))
else v)
for k, v in A["params"].items()}
pb = {k: (v * (1.0 + rng.normal(0.0, 0.01)) if isinstance(v, (int, float))
else v)
for k, v in B["params"].items()}
newparams = collidedict(pa, pb, rng, exactcancel)
newops = collideops(A["operators"], B["operators"], rng, exactcancel)
# Multipliers
if grid_scaling:
newparams["injectionamp"] = float(newparams.get("injectionamp", newparams.get("injection_amp", 1e-4))) * float(injmult)
if "diffusionnu" in newparams:
newparams["diffusionnu"] = float(newparams.get("diffusionnu", 1e-3)) * float(couplingmult)
else:
newparams["kappa"] = float(newparams.get("kappa", 1e-3)) * float(couplingmult)
else:
newparams["injectionamp"] = float(newparams.get("injectionamp", newparams.get("injection_amp", 1e-4)))
if "diffusionnu" not in newparams:
newparams["kappa"] = float(newparams.get("kappa", 1e-3))
newparams["xi"] = float(clamp_val(newparams.get("xi", 0.0), 1e-4, 1.0))
newparams["lambda"] = float(clamp_val(newparams.get("lambda", 0.0), 1e-4, 1.0))
candidates.append({"params": newparams, "operators": newops})
return candidates

def EVOLVEZSFtest(zs: Dict[str, Any], params: Dict[str, Any], rng: np.random.Generator, dt: float = 1e-4) -> Dict[str, Any]:
xi = float(params.get("xi", zs.get("xi", 0.021)))
lam = float(params.get("lambda", zs.get("lambda", 0.121)))
phi = float(zs.get("phi", 0.0))
linearxi = float(params.get("coefxi", params.get("linearcoefxi", -0.5)))
linearlam = float(params.get("coeflambda", params.get("linearcoeflambda", -0.3)))
linearterm = linearxi * xi + linearlam * lam
Gamma = float(params.get("Gamma", params.get("dissipative_Gamma", 5e-4)))
dissipative = -Gamma * phi
delta = float(params.get("delta", 1e-3))
nonlinear = -delta * (phi * 3.0)
eta = float(params.get("etaamp", 1e-5 if not args.high_noise else 5e-5))
noise = eta * rng.normal(0.0, 1.0) / math.sqrt(max(dt, 1e-16))
thetaplus = float(params.get("thetaplus", 0.35 if args.lower_threshold else 0.5))
thetaminus = float(params.get("thetaminus", -0.35 if args.lower_threshold else -0.5))
inj = float(params.get("injectionamp", 0.0) or 0.0)
injection = 0.0
near = float(params.get("near_margin", 0.12))
if (thetaplus - near) <= phi < thetaplus:
injection += inj * (1.0 + 5.0 * xi)
if thetaminus < phi <= (thetaminus + near):
injection -= inj * (1.0 + 5.0 * xi)
mixgain = float(params.get("mixgain", params.get("mix_alpha", 5.0)))
if abs(phi) < float(params.get("mix_zone", 0.25)):
injection += mixgain * eta * rng.normal(0.0, 1.0)
phinew = phi + dt * (linearterm + dissipative + nonlinear) + math.sqrt(max(dt, 1e-16)) * noise + injection
# TRIT map + hold
if phinew >= thetaplus:
trit = 1
elif phinew <= thetaminus:
trit = -1
else:
trit = 0
if trit == 1:
hold = float(params.get("hold_strength", 0.12))
phinew = phinew * (1.0 - hold) + 1.0 * hold
elif trit == -1:
hold = float(params.get("hold_strength", 0.12))
phinew = phinew * (1.0 - hold) - 1.0 * hold
zs_out = dict(zs)
zs_out["phi"] = float(phinew)
zs_out["tritstate"] = int(trit)
zs_out["xi"] = xi
zs_out["lambda"] = lam
zs_out["entropy"] = zs_out["tritstate"] * xi
return zs_out

def validate_candidates(cands: List[Dict[str, Any]], rng: np.random.Generator, steps: int = 500, runs: int = 4, debug: bool = False) -> List[Dict[str, Any]]:
results = []
for idx, c in enumerate(cands):
success = 0
finals: List[int] = []
for r in range(runs):
rgen = np.random.default_rng(int(rng.integers(1, 10000)))
zs = {"phi": 0.0, "tritstate": 0, "xi": c["params"].get("xi", 0.021),
"lambda": c["params"].get("lambda", 0.121)}
for _ in range(steps):
zs = EVOLVEZSFtest(zs, c["params"], rgen)
finals.append(zs["tritstate"])
if zs["tritstate"] != 0:
success += 1
rate = success / runs
results.append({"idx": idx, "rate": rate, "finals": finals, "params": c["params"]})
if debug and idx < 6:
print(f"[validate] idx={idx} rate={rate:.2f} keys={list(c['params'].keys())[:6]}")
return results

-------------------------

Stabilizer and single-point adaptive injection

-------------------------

def applystabilizingops(zs: Dict[str, Any], rng: np.random.Generator, params: Dict[str, Any] = None) -> Dict[str, Any]:
if params is None:
params = {}
phi = float(zs.get("phi", 0.0))
xi = float(zs.get("xi", 0.021))
thetaplus = float(params.get("thetaplus", 0.35 if args.lower_threshold else 0.5))
thetaminus = float(params.get("thetaminus", -0.35 if args.lower_threshold else -0.5))
softclip = float(params.get("softclip", 1.0))
clipstrength = float(params.get("clipstrength", 0.15))
if abs(phi) > softclip:
phi = phi * (1.0 - clipstrength) + math.copysign(softclip, phi) * clipstrength
biasamp = float(params.get("injectionamp", 0.06))
nearmargin = float(params.get("nearmargin", 0.12))
if (thetaplus - nearmargin) <= phi < thetaplus:
phi += biasamp * (1.0 + 5.0 * xi)
if thetaminus < phi <= (thetaminus + nearmargin):
phi -= biasamp * (1.0 + 5.0 * xi)
mixzone = float(params.get("mixzone", 0.25))
mixgain = float(params.get("mixgain", 10.0))
if abs(phi) < mixzone:
noiseamp = float(params.get("etaamp", 5e-5))
noise = noiseamp * rng.normal(0.0, 1.0)
drive = float(params.get("drive_bias", 0.0))
phi += (noise * mixgain) + drive * 1e-3
trit = int(zs.get("tritstate", 0))
holdstrength = float(params.get("holdstrength", 0.12))
if trit == 1:
phi = phi * (1.0 - holdstrength) + 1.0 * holdstrength
elif trit == -1:
phi = phi * (1.0 - holdstrength) - 1.0 * holdstrength
phi = max(min(phi, 1.2), -1.2)
zs_out = dict(zs)
zs_out["phi"] = float(phi)
return zs_out

def EVOLVEZSFwithadaptiveinjection(zs: Dict[str, Any], params: Dict[str, Any], dt: float = 1e-4, rng: np.random.Generator = None) -> Dict[str, Any]:
if rng is None:
rng = rng_default(zs.get("seed", 42))
xi = float(zs.get("xi", params.get("xi", 0.021)))
lam = float(zs.get("lambda", params.get("lambda", 0.121)))
phi = float(zs.get("phi", 0.0))
coefxi = float(params.get("coefxi", -0.5))
coeflambda = float(params.get("coeflambda", -0.3))
linearterm = coefxi * xi + coeflambda * lam
Gamma = float(params.get("Gamma", 5e-4))
dissipative_term = -Gamma * phi
delta = float(params.get("delta", 1e-3))
nonlinear_term = -delta * (phi * 3.0)
etaamp = float(params.get("etaamp", 1e-5 if not args.high_noise else 5e-5))
noise_term = etaamp * rng.normal(0.0, 1.0) / math.sqrt(max(dt, 1e-16))
# Sampled base injection
residual_scalar = max(0.0, phi * float(params.get("Nmcsample", args.Nmcsample)))
samples = sample_collision_residual(residual_scalar, int(params.get("Nmcsample", args.Nmcsample)), rng)
inj_sample = (float(np.mean(samples)) * float(params.get("eta_in", 0.05))) if samples.size > 0 else float(params.get("injectionamp", 1e-4))
injbase = inj_sample * (1.0 + 5.0 * xi)
# Adaptive injection
injadapt = float(zs.get("injectionadaptive", 0.0))
Kp = float(params.get("injcontrollergain", args.Kp))
Imax = float(params.get("injmax", args.Imax))
thetaplus = float(params.get("thetaplus", 0.35 if args.lower_threshold else 0.5))
thetaminus = float(params.get("thetaminus", -0.35 if args.lower_threshold else -0.5))
thetatarget = thetaplus + float(args.theta_target_offset)
previnj = float(zs.get("injprev", 0.0))
Kd = float(params.get("inj_damping", 0.01))
error = thetatarget - phi
injadapt += Kp * error - Kd * (injadapt - previnj)
injadapt = max(min(injadapt, Imax), -Imax)
injection = injbase + injadapt
if abs(phi) < float(params.get("mix_zone", 0.25)):
injection += float(params.get("mixgain", 5.0)) * etaamp * rng.normal(0.0, 1.0)
phinew = phi + dt * (linearterm + dissipative_term + nonlinear_term) + math.sqrt(max(dt, 1e-16)) * noise_term + injection
# TRIT map + soft-hold + adaptive bump
if phinew >= thetaplus:
trit = 1
hold = float(params.get("hold_strength", 0.12))
phinew = phinew * (1.0 - hold) + 1.0 * hold
elif phinew <= thetaminus:
trit = -1
hold = float(params.get("hold_strength", 0.12))
phinew = phinew * (1.0 - hold) - 1.0 * hold
else:
trit = 0
if trit == 1:
injadapt = max(injadapt, float(params.get("injhold", args.inj_hold)))
injadapt *= 0.98
prevtrit = int(zs.get("tritstate", 0))
if prevtrit == 1 and trit == 0 and phinew < thetaplus - 0.05:
injadapt += min(0.1, Imax * 0.2)
phinew = max(min(phinew, 1.2), -1.2)
zs_out = dict(zs)
zs_out["phi"] = float(phinew)
zs_out["tritstate"] = int(trit)
zs_out["injectionadaptive"] = float(injadapt)
zs_out["injprev"] = float(injadapt)
zs_out["xi"] = xi
zs_out["lambda"] = lam
zs_out["entropy"] = zs_out["tritstate"] * xi
if args.debug:
print(f"ADAPT step: phi={phi:.6e} -> {phinew:.6e}, injbase={injbase:.3e}, injadapt={injadapt:.3e}, trit={trit}")
return zs_out

-------------------------

Operator class (grid) and base ops

-------------------------

class Operator:
def init(self, name: str, params: Dict[str, Any], apply_fn):
self.name = name
self.params = dict(params)
self.apply_fn = apply_fn
self.next_op: Operator | None = None

def apply(self, state: np.ndarray, rng: np.random.Generator = None) -> Tuple[np.ndarray, Dict[str, Any]]:
    if rng is None:
        rng = rng_default()
    result, info = self.apply_fn(state, self.params, rng)
    if self.next_op is not None:
        result, next_info = self.next_op.apply(result, rng)
        info.update(next_info)
    return result, info

def chain(self, next_op: "Operator") -> "Operator":
    self.next_op = next_op
    return self

def op_diffusion(state, params, rng):
dx = float(params.get("dx", 1.0))
dt = float(params.get("dt", 1e-5))
nu = float(params.get("nu", 1e-4)) * float(params.get("coupling_mult", args.coupling_mult))
lap = laplacian_1d(state, dx)
state_new = state + nu * lap * dt
return state_new, {"energy_diffusion": energy_of_state(state_new, dx), "nu_used": nu}

def op_advection(state, params, rng):
dx = float(params.get("dx", 1.0))
dt = float(params.get("dt", 1e-5))
v = float(params.get("v", 0.097))
state_avg = float(np.mean(state))
v = v * (1.0 + 0.1 * state_avg) # adaptive speed
F = v * state
F_im = np.roll(F, 1)
conv_term = -(dt / dx) * (F - F_im)
state_new = state + conv_term
return state_new, {"energy_advection": energy_of_state(state_new, dx), "v_used": v}

def op_noise(state, params, rng):
dx = float(params.get("dx", 1.0))
sigma = float(params.get("sigma", 1e-6)) * (5 if params.get("high_noise", False) else 1)
noise = rng.normal(scale=sigma, size=state.shape)
state_new = state + noise
return state_new, {"energy_noise": energy_of_state(state_new, dx), "sigma_used": sigma}

def op_projection(state, params, rng):
dx = float(params.get("dx", 1.0))
clip = float(params.get("clip", 1.023))
soft_clip = float(params.get("soft_clip", 1.2))
state_new = np.where(np.abs(state) > soft_clip,
state * (1.0 - 0.15) + np.sign(state) * soft_clip * 0.15, state)
state_new = np.clip(state_new, -clip, clip)
return state_new, {"energy_projection": energy_of_state(state_new, dx),
"clipped_count": int(np.sum(np.abs(state_new) >= clip))}

def op_adaptive_injection(state, params, rng):
dx = float(params.get("dx", 1.0))
grid_size = int(state.shape[0])
center = grid_size // 2
width = max(1.0, float(params.get("width", max(1, grid_size // 20))))
x = np.arange(grid_size)
kernel = np.exp(-0.5 * ((x - center) / width) ** 2)
kernel = kernel / (kernel.sum() + 1e-12)
phi_prev = float(np.mean(state))
thetaplus = 0.35 if params.get("lower_threshold", False) else 0.5
theta_target = thetaplus + float(args.theta_target_offset)
Kp = float(params.get("Kp", args.Kp))
Kd = float(params.get("Kd", 0.01))
Imax = float(params.get("Imax", args.Imax))
inj_prev = float(params.get("inj_prev", 0.0))
inj_prev_prev = float(params.get("inj_prev_prev", 0.0))
error = theta_target - phi_prev
inj_adapt = inj_prev + Kp * error - Kd * (inj_prev - inj_prev_prev)
inj_adapt = clamp_val(inj_adapt, -Imax, Imax)
inj_base = float(params.get("injectionamp", 1e-4)) * (1.0 + 5.0 * float(params.get("xi", 0.021))) * float(params.get("inj_mult", args.inj_mult))
mix_zone = float(params.get("mix_zone", 0.25))
if abs(phi_prev) < mix_zone:
inj_base += float(params.get("mixgain", 5.0)) * float(params.get("etaamp", 1e-5)) * rng.normal(0.0, 1.0)
injection = inj_base + inj_adapt
state_new = state + injection * kernel
trit = 1 if phi_prev >= thetaplus else (-1 if phi_prev <= -thetaplus else 0)
if trit == 1:
inj_adapt = max(inj_adapt, float(params.get("inj_hold", args.inj_hold)))
inj_adapt *= 0.98
params["inj_prev_prev"] = inj_prev
params["inj_prev"] = inj_adapt
return state_new, {"energy_injection": energy_of_state(state_new, dx), "inj_adapt": inj_adapt, "trit": trit}

def op_stabilize(state, params, rng):
dx = float(params.get("dx", 1.0))
phi = float(np.mean(state))
thetaplus = float(params.get("thetaplus", 0.35 if args.lower_threshold else 0.5))
soft_clip = float(params.get("softclip", 1.0))
clip_strength = float(params.get("clipstrength", 0.15))
if abs(phi) > soft_clip:
phi = phi * (1.0 - clip_strength) + math.copysign(soft_clip, phi) * clip_strength
E_prev = energy_of_state(state, dx)
scale = phi / (float(np.mean(state)) + 1e-12)
state_new = state * scale
E_new = energy_of_state(state_new, dx)
Z_proxy = abs(E_new - E_prev) / max(float(params.get("Z_threshold", args.Z_threshold)), abs(E_prev))
if Z_proxy > float(params.get("Z_threshold", args.Z_threshold)):
state_new = state_new * float(params.get("safetydamp_coef", args.safetydamp_coef))
return state_new, {"energy_stabilize": energy_of_state(state_new, dx), "Z_proxy": Z_proxy}

-------------------------

Evolved composite operator chain

-------------------------

def create_evolved_best_operator(dx: float) -> Operator:
default_ops = [
Operator("advection", {"v": 0.097, "dx": dx, "dt": args.dt_micro}, op_advection),
Operator("noise", {"sigma": args.sigma_noise, "dx": dx, "high_noise": args.high_noise}, op_noise),
Operator("diffusion", {"nu": 9.87e-4, "dx": dx, "dt": args.dt_micro, "coupling_mult": args.coupling_mult}, op_diffusion),
Operator("adaptive_injection", {
"injectionamp": 9.8e-5, "inj_mult": args.inj_mult, "dx": dx,
"lower_threshold": args.lower_threshold,
"xi": 0.021, "mixgain": 5.0, "etaamp": 1e-5, "Kp": args.Kp, "Imax": args.Imax
}, op_adaptive_injection),
Operator("projection", {"clip": 1.023, "soft_clip": 1.2, "dx": dx}, op_projection),
Operator("stabilize", {"Z_threshold": args.Z_threshold, "dx": dx, "safetydamp_coef": args.safetydamp_coef}, op_stabilize),
]
fused_op = default_ops[0]
for op in default_ops[1:]:
fused_op = fused_op.chain(op)
return fused_op

-------------------------

Grid evolution using operator chain

-------------------------

def EVOLVEZSFwithadaptiveinjection_grid(state: np.ndarray, params: Dict[str, Any], rng: np.random.Generator, dx: float, dt: float) -> Tuple[np.ndarray, Dict[str, Any]]:
op_chain = create_evolved_best_operator(dx)
state_new, info = op_chain.apply(state, rng)
phi = float(np.mean(state_new))
thetaplus = float(params.get("thetaplus", 0.35 if args.lower_threshold else 0.5))
thetaminus = -thetaplus
trit = 1 if phi >= thetaplus else (-1 if phi <= thetaminus else 0)
info["trit_state"] = trit
info["phi"] = phi
info["dt_used"] = dt
return state_new, info

-------------------------

FUSE (single-point and grid)

-------------------------

def FUSE(zs1: Dict[str, Any], zs2: Dict[str, Any], T_local: float = 300.0) -> Dict[str, Any]:
b1 = int(zs1.get("tritstate", zs1.get("trit_state", 0)))
b2 = int(zs2.get("tritstate", zs2.get("trit_state", 0)))
xi1 = float(zs1.get("xi", 0.021))
xi2 = float(zs2.get("xi", 0.021))
fused: Dict[str, Any] = {}
xor = (b1 ^ b2) % 2
if xor != 0:
delta_xi = (xi1 - xi2) / 2.0
zs1["xi"] = xi1 - delta_xi
zs2["xi"] = xi2 + delta_xi
sum_b = (b1 + b2) % 3
if sum_b != 0:
delta_S = ((b1 + b2) - 0) * zs1.get("xi", xi1)
deltaE = abs(delta_S) * T_local
fused["energy"] = zs1.get("energy", 0.0) + zs2.get("energy", 0.0) + deltaE
fused_trit = int(round((b1 + b2) / 2.0)) if (b1 + b2) != 0 else 0
fused["tritstate"] = fused_trit
fused["xi"] = (zs1.get("xi", xi1) + zs2.get("xi", xi2)) / 2.0
fused["entropy"] = fused["tritstate"] * fused["xi"]
if args.debug:
print(f"DEBUG FUSE: b1={b1}, b2={b2}, fusedtrit={fused_trit}, fused_xi={fused['xi']:.6e}")
return fused

def FUSE_grid(zs_list: List[Dict[str, Any]], T_local: float, dx: float) -> Dict[str, Any]:
if len(zs_list) < 2:
return zs_list[0] if zs_list else {"state": np.zeros(10), "energy": 0.0, "trit_state": 0}
energies = [energy_of_state(zs["state"], dx) for zs in zs_list]
weights = [1.0 / max(E, 1e-12) for E in energies]
weights = [w / sum(weights) for w in weights]
fused_state = np.sum([zs["state"] * w for zs, w in zip(zs_list, weights)], axis=0)
trits = [zs.get("trit_state", 0) for zs in zs_list]
fused_trit = int(round(np.sum([t * w for t, w in zip(trits, weights)])))
fused_energy = energy_of_state(fused_state, dx)
delta_S = float(np.sum([zs.get("entropy", 0.0) * w for zs, w in zip(zs_list, weights)]))
fused_energy += abs(delta_S) * T_local
return {"state": fused_state, "energy": fused_energy, "trit_state": fused_trit, "entropy": delta_S, "weights": weights}

-------------------------

DARKMATTER_TRIGGER (single-point and grid)

-------------------------

def DARKMATTERTRIGGER(zs: Dict[str, Any], couplingsection: float, Veff: float, dt: float = 1e-4, rng: np.random.Generator = None, maxsteps: int = 1000, params: Dict[str, Any] = None) -> Tuple[Dict[str, Any], List[str]]:
if rng is None:
rng = rng_default(zs.get("seed", 42))
if params is None:
params = {}
log: List[str] = []
if couplingsection <= 1e-40:
log.append("coupling_section below threshold; no trigger")
return zs, log
zs = dict(zs)
zs["lambda"] = 0.121
log.append("lambda set to 0.121")
zs["phi"] = zs.get("phi", 0.0) + 0.1
log.append(f"phi biased +0.1 -> {zs['phi']:.6e}")
evo_params = params.get("evoparams", {})
for step in range(maxsteps):
zs = EVOLVEZSFwithadaptiveinjection(zs, evo_params, dt=dt, rng=rng)
if zs.get("tritstate", 0) == 1:
log.append(f"Reached trit=1 at step {step}")
break
delta_S = 4e-4
T_local = 300.0
deltaE = delta_S * T_local
deltarho = deltaE / (Veff * (3e8 ** 2))
deltaR = 8.0 * math.pi * 6.67e-11 * deltarho / (3e8 ** 2)
zs["entropy"] = delta_S
zs["energy"] = zs.get("energy", 0.0) + deltaE
zs["curvature"] = zs.get("curvature", 0.0) + deltaR
log.append(f"trigger success: injected energy={deltaE:.6e} J, curvature change={deltaR:.6e} m^-1")
return zs, log

def DARKMATTER_TRIGGER_grid(zs: Dict[str, Any], coupling_section: float, Veff: float, rng: np.random.Generator, dx: float) -> Tuple[Dict[str, Any], List[str]]:
log: List[str] = []
if coupling_section <= 1e-40:
log.append("coupling_section below threshold; no trigger")
return zs, log
state = zs.get("state", np.zeros(10))
zs["lambda"] = 0.121
log.append("lambda set to 0.121")
phi_biased = float(np.mean(state)) + 0.1
scale = phi_biased / (float(np.mean(state)) + 1e-12)
state = state * scale
log.append(f"phi biased +0.1 -> {phi_biased:.6e}")
evo_params = zs.get("evoparams", {})
max_steps = 1000
trit_state = 0
for step in range(max_steps):
state, info = EVOLVEZSFwithadaptiveinjection_grid(state, evo_params, rng, dx, args.dt_micro)
trit_state = info["trit_state"]
if trit_state == 1:
log.append(f"Reached trit=1 at step {step}")
break
delta_S = 4e-4
T_local = 300.0
delta_E = delta_S * T_local
delta_rho = delta_E / (Veff * (3e8 ** 2))
delta_R = 8.0 * math.pi * 6.67e-11 * delta_rho / (3e8 ** 2)
E_current = energy_of_state(state, dx)
E_new = E_current + delta_E
state = state * math.sqrt(E_new / max(E_current, 1e-12))
Z_proxy = abs(E_new - E_current) / max(args.Z_threshold, abs(E_current))
if Z_proxy > args.Z_threshold:
state = state * args.safetydamp_coef
log.append(f"Energy injection exceed Z_threshold: {Z_proxy:.3e}, damp state")
zs["state"] = state
zs["entropy"] = delta_S
zs["energy"] = E_new
zs["curvature"] = zs.get("curvature", 0.0) + delta_R
zs["trit_state"] = trit_state
log.append(f"trigger success: injected energy={delta_E:.6e} J, curvature change={delta_R:.6e} m^-1")
return zs, log

-------------------------

Quantum module + carbon synthesis (single and grid)

-------------------------

def quantumsecondquantizescaled(k: float, omegak: float, debug: bool = False, clampforsynthesis: bool = False, scale: float = None, autoscale: bool = False, targetxi: float = 0.021) -> Dict[str, Any]:
hbar = 1.05457e-34
creationop = math.sqrt(hbar / (2.0 * omegak)) if omegak > 0 else 0.0
xiraw = creationop * 0.021
xi = xiraw
if autoscale and xiraw > 0:
if xiraw != 0:
scaleauto = targetxi / xiraw
xi = xiraw * scaleauto
else:
xi = xiraw * (targetxi / xiraw if xiraw != 0 else 1.0)
if scale is not None:
xi = xi * scale
if clampforsynthesis:
xi = clamp_val(xi, 0.019, 0.023)
xi = max(xi, 1e-3)
tritstate = 1 if creationop > 0 else -1
return {"xi": xi, "lambda": 0.121, "entropy": 0.0, "tritstate": tritstate}

def ZSFCARBONSYNTHESISdemo(scale: float = None, clamp: bool = False, autoscale: bool = True, debug: bool = False) -> Dict[str, Any]:
candidates = [1.2e15, 1.0e15, 1.5e15, 8.0e14]
carbon_zs = None
for ok in candidates:
cz = quantumsecondquantizescaled(0.5, ok, debug=debug,
clampforsynthesis=False, scale=scale, autoscale=autoscale, targetxi=0.021)
if debug:
print(f"TRY omega_k={ok:.6e} -> xi={cz['xi']:.6e}")
if 0.019 <= cz["xi"] <= 0.023:
carbon_zs = cz
if debug:
print(f"SELECTED omega_k={ok} with xi={cz['xi']:.6e}")
break
if carbon_zs is None:
carbon_zs = quantumsecondquantizescaled(0.5, candidates[0],
debug=debug, clampforsynthesis=clamp, scale=scale, autoscale=autoscale, targetxi=0.021)
if debug:
print(f"FINAL carbon xi after fallback: {carbon_zs['xi']:.6e}")
catalystzs = {"xi": 0.021, "lambda": 0.121, "entropy": -0.0002, "tritstate": -1, "energy": 0.0}
fused = FUSE(carbon_zs, catalystzs, T_local=3300.0)
carbonstrength = 11.2 if fused["tritstate"] == 0 else 12.0
return {"strengthGPa": carbonstrength, "fusedtrit": fused["tritstate"], "fused": fused, "carbonzs": carbon_zs}

def ZSF_CARBON_SYNTHESIS_grid(dx: float, debug: bool = False) -> Dict[str, Any]:
res = ZSFCARBONSYNTHESISdemo(autoscale=True, debug=debug)
Nx = int(args.grid_Nx)
carbon_state = res["carbonzs"]["xi"] * np.ones(Nx, dtype=float)
catalyst_state = 0.05 * np.exp(-0.5 * (np.linspace(-args.Lx, args.Lx, Nx) / 0.1) ** 2)
fused_grid = FUSE_grid([
{"state": carbon_state, "entropy": 0.0, "trit_state": res["fusedtrit"]},
{"state": catalyst_state, "entropy": -0.0002, "trit_state": -1}
], T_local=300.0, dx=dx)
injection_source = fused_grid["state"] * 0.1
return {"strengthGPa": res["strengthGPa"], "fused_trit": fused_grid["trit_state"],
"fused": fused_grid, "carbon_zs": res["carbonzs"], "injection_source": injection_source}

-------------------------

Single-point demo

-------------------------

def unitpointevolutiondemo(Nsteps: int = None, seed: int = 42, paramsoverride: Dict[str, Any] = None) -> Dict[str, List[float]]:
if Nsteps is None:
Nsteps = args.steps
rng = rng_default(seed)
params = {
"Gamma": 5e-4, "delta": 1e-3, "etaamp": 1e-5, "xi": 0.021, "lambda": 0.121,
"thetaplus": 0.35 if args.lower_threshold else 0.5, "thetaminus": -0.35 if args.lower_threshold else -0.5,
"dt": 1e-4, "Nmcsample": args.Nmcsample, "eta_in": 0.05, "injectionamp": 1e-4 * args.inj_mult,
"mixgain": 5.0 * args.inj_mult, "mix_zone": 0.25, "hold_strength": 0.12,
"stabilize": args.stabilize, "injcontrollergain": args.Kp, "injmax": args.Imax, "injhold": args.inj_hold
}
params["kappa"] = float(params.get("kappa", 1e-3)) * float(args.coupling_mult)
if paramsoverride:
params.update(paramsoverride)
zs = {"xi": params["xi"], "lambda": params["lambda"], "phi": 0.0, "tritstate": 0, "entropy": 0.0,
"seed": seed, "injectionadaptive": 0.0, "injprev": 0.0}
history = {"phi": [], "trit": [], "injadapt": []}
for _ in range(Nsteps):
zs = EVOLVEZSFwithadaptiveinjection(zs, params, dt=params["dt"], rng=rng)
if params.get("stabilize", False) or args.stabilize:
zs = applystabilizingops(zs, rng, params.get("stabilize_params", {}))
history["phi"].append(zs["phi"])
history["trit"].append(zs["tritstate"])
history["injadapt"].append(zs.get("injectionadaptive", 0.0))
return history

-------------------------

Regression Test

-------------------------

def regression_test():
seed = 12345
baseline = unitpointevolutiondemo(Nsteps=200, seed=seed, paramsoverride=None)
merged = unitpointevolutiondemo(Nsteps=200, seed=seed, paramsoverride=None)
phi_diff = abs(baseline["phi"][-1] - merged["phi"][-1])
trit_match = baseline["trit"][-1] == merged["trit"][-1]
print("Regression test: final phi diff =", phi_diff, "trit match =", trit_match)
return {"phi_diff": phi_diff, "trit_match": trit_match,
"baseline_final_phi": baseline["phi"][-1], "merged_final_phi": merged["phi"][-1]}

-------------------------

Original Grid Simulation

-------------------------

BASE_PARAMS = {
"seed": args.seed, "T_total": args.T_total, "dt_micro": args.dt_micro, "dt_macro": args.dt_macro,
"grid_Nx": args.grid_Nx, "Lx": args.Lx, "v0": args.v0, "alpha_s": args.alpha_s, "nu": args.nu,
"gamma_env": 0.05, "kappa_proj": args.kappa_proj, "eta_in": 0.05, "eta_heat": 0.10,
"eta_fus": 0.05, "sigma_fus": 0.03, "Nmcsample": args.Nmcsample, "gammak": 5e-4,
"sigma_noise": args.sigma_noise, "Z_threshold": args.Z_threshold, "safetydamp_coef": args.safetydamp_coef,
"maxGtotscalar": args.maxGtotscalar, "min_denominator": args.min_denominator, "maxinjectionfrac": args.maxinjectionfrac,
"adaptivedt_allowed": True, "cfl_max": args.cfl_max, "maxabsorbenergy_frac": args.maxabsorbenergy_frac,
"debugstopon_nan": True, "maxclipvalue": 1e6,
}

def upwind_step_1d(S, V, dtlocal, dxlocal, Ginlocal=None, gamma_env=0.0, nu=0.0):
if Ginlocal is None:
Ginlocal = np.zeros_like(S)
F = V * S
F_im = np.roll(F, 1)
convterm = -(dtlocal / dxlocal) * (F - F_im)
diffterm = nu * (dtlocal / (dxlocal * dxlocal)) * laplacian_1d(S, dxlocal)
srcterm = dtlocal * (Ginlocal - gamma_env * S)
newS = S + convterm + diffterm + srcterm
newS = 0.995 * newS + 0.005 * np.roll(newS, 1)
return newS

def run_simulation(params, use_evolved=True, verbose=False):
p = copy.deepcopy(params)
rng = rng_default(int(p["seed"]))
Nx = int(p["grid_Nx"])
x = np.linspace(-p["Lx"], p["Lx"], Nx)
dx = 2.0 * p["Lx"] / max(1, Nx - 1)
S = 0.1 + 0.01 * np.exp(-0.5 * (x / 0.2)**2)
V = p["v0"] * (1.0 + p["alpha_s"] * S)
Nexpect = 0.02
Eheat = 0.0
Emicro0 = float(Nexpect)
Emacro0 = energy_of_state(S, dx)
E0 = Emicro0 + Emacro0 + Eheat
dt = float(p["dt_micro"])
times = np.arange(0.0, float(p["T_total"]), dt)
steps = len(times)
macrosync = max(1, int(round(p["dt_macro"] / dt)))
projkernel = np.exp(-0.5 * (x / 0.1)**2)
projkernel /= projkernel.sum()
collkernel = np.exp(-0.5 * (x / p["sigma_fus"])**2)
collkernel /= collkernel.sum()
hist = {
"t": np.zeros(steps), "N_expect": np.zeros(steps), "E_micro": np.zeros(steps),
"E_macro": np.zeros(steps), "E_heat": np.zeros(steps), "Z_proxy": np.zeros(steps),
"S_center": np.zeros(steps),
}
localdt = dt
evolved_op = create_evolved_best_operator(dx=dx) if use_evolved else None
start_time = time.time()
for k, t in enumerate(times):
xi = xi_of(t)
dNdrive = 1e-2 * xi * (1.0 - np.clip(Nexpect, 0.0, 1.0)) * localdt
dNdiss = -p["gammak"] * Nexpect * localdt
noise = rng.normal(scale=math.sqrt(max(p["sigma_noise"], 0.0)) * math.sqrt(localdt))
Nexpect = max(0.0, Nexpect + dNdrive + dNdiss + noise)
Ncollected = p["kappa_proj"] * Nexpect
Nresidual = max(0.0, Nexpect - Ncollected)
samples = sample_collision_residual(Nresidual, p["Nmcsample"], rng)
coll_count = samples.size
if coll_count > 0:
deltain = p["eta_in"] * samples
deltaheat = p["eta_heat"] * samples
deltafus = p["eta_fus"] * samples
deltainavg = float(deltain.sum() / coll_count)
deltaheatavg = float(deltaheat.sum() / coll_count)
deltafusavg = float(deltafus.sum() / coll_count)
deltainavg = min(deltainavg, p["maxinjectionfrac"] * max(1e-12, Nexpect))
deltaheatavg = min(deltaheatavg, p["maxinjectionfrac"] * max(1e-12, Nexpect))
deltafusavg = min(deltafusavg, p["maxinjectionfrac"] * max(1e-12, Nexpect))
deltainavg = float(np.clip(deltainavg, -1e-3, 1e-3))
deltaheatavg = float(np.clip(deltaheatavg, -1e-3, 1e-3))
deltafusavg = float(np.clip(deltafusavg, -1e-3, 1e-3))
else:
deltainavg = deltaheatavg = deltafusavg = 0.0
Gtotscalar = Ncollected + deltainavg
Gtotscalar = min(Gtotscalar, p["maxGtotscalar"])
Gproj = Gtotscalar * projkernel
Gfus = (1.0 - p["eta_fus"]) * deltafusavg * collkernel
Gtotal = Gproj + Gfus
# Adjust time step by CFL condition
if p["adaptivedt_allowed"]:
try:
localdt = adjust_dt_by_cfl_1d(V, dx, localdt, p["cfl_max"], 1e-10, p["dt_micro"] * 10)
except Exception:
localdt = max(1e-10, localdt * 0.5)
# Apply operator (evolved or original)
if (k % macrosync) == 0:
if use_evolved and evolved_op is not None:
S, _ = evolved_op.apply(S, rng)
else:
S = upwind_step_1d(S, V, macrosync * localdt, dx, Ginlocal=Gtotal, gamma_env=p["gamma_env"], nu=p["nu"])
# Update velocity
V = p["v0"] * (1.0 + p["alpha_s"] * S)
# Update energy components
maxallowed2 = max(p["maxinjectionfrac"] * max(1e-12, Nexpect), p["min_denominator"])
heatincrement = min(deltaheatavg, maxallowed2)
newmicromass = min(deltafusavg, maxallowed2)
Eheat += heatincrement
Nexpect = max(0.0, Nexpect - heatincrement + newmicromass)
Emicro = float(Nexpect)
Emacro = energy_of_state(S, dx)
Etotal = Emicro + Emacro + Eheat
# Check energy drift
Zproxy = abs(Etotal - E0) / max(p["min_denominator"], abs(E0))
if Zproxy > p["Z_threshold"]:
deltaE = Etotal - E0
maxabsorb = p.get("maxabsorbenergy_frac", 0.1) * max(abs(E0), p["min_denominator"])
absorb = math.copysign(min(abs(deltaE), maxabsorb), deltaE)
Eheat -= absorb
S = S * p["safetydamp_coef"]
Nexpect = Nexpect * p["safetydamp_coef"]
# Record history
hist["t"][k] = t
hist["N_expect"][k] = Nexpect
hist["E_micro"][k] = Emicro
hist["E_macro"][k] = Emacro
hist["E_heat"][k] = Eheat
hist["Z_proxy"][k] = Zproxy
hist["S_center"][k] = float(S[len(S)//2])
# Debug: stop on NaN
if p["debugstopon_nan"]:
if not np.isfinite(Etotal) or np.isnan(Zproxy) or np.any(~np.isfinite(S)):
return hist, time.time() - start_time, True
runtime = time.time() - start_time
return hist, runtime, False

def run_comparison(nruns=10):
seeds = [int(BASE_PARAMS["seed"]) + i for i in range(nruns)]
results = {"evolved": [], "original": []}
for use_evolved in [True, False]:
label = "evolved" if use_evolved else "original"
for s in seeds:
p = copy.deepcopy(BASE_PARAMS)
p["seed"] = s
hist, runtime, failed = run_simulation(p, use_evolved=use_evolved, verbose=False)
E = hist["E_macro"] + hist["E_micro"] + hist["E_heat"]
E0 = E[0]
errE = np.abs(E - E0) / max(1e-12, abs(E0))
mean_err = float(np.mean(errE))
max_err = float(np.max(errE))
drift = float((E[-1] - E0) / max(1e-12, abs(E0)))
results[label].append({
"mean_err": mean_err, "max_err": max_err, "drift": drift, "nan": failed, "runtime": runtime
})
print(f"{label} seed={s}: mean_err={mean_err:.3e}, max_err={max_err:.3e}, drift={drift:.3e}, nan={failed}, time={runtime:.3f}s")
def summarize(list_of_dicts):
mean_errs = [d["mean_err"] for d in list_of_dicts]
drifts = [d["drift"] for d in list_of_dicts]
nans = sum(1 for d in list_of_dicts if d["nan"])
times = [d["runtime"] for d in list_of_dicts]
return {
"mean_err_mean": mean(mean_errs), "mean_err_std": stdev(mean_errs) if len(mean_errs) > 1 else 0.0,
"drift_mean": mean(drifts), "drift_std": stdev(drifts) if len(drifts) > 1 else 0.0,
"nan_count": nans, "time_mean": mean(times)
}
sum_e = summarize(results["evolved"])
sum_o = summarize(results["original"])
print("\nSummary (evolved vs original - original grid)")
print(f" mean_err: {sum_e['mean_err_mean']:.3e} ± {sum_e['mean_err_std']:.3e} vs {sum_o['mean_err_mean']:.3e} ± {sum_o['mean_err_std']:.3e}")
print(f" drift: {sum_e['drift_mean']:.3e} ± {sum_e['drift_std']:.3e} vs {sum_o['drift_mean']:.3e} ± {sum_o['drift_std']:.3e}")
print(f" NaN failures: {sum_e['nan_count']} / {nruns} vs {sum_o['nan_count']} / {nruns}")
print(f" Avg runtime (s): {sum_e['time_mean']:.3f} vs {sum_o['time_mean']:.3f}")
improvement_factor = (sum_o["mean_err_mean"] / sum_e["mean_err_mean"]) if sum_e["mean_err_mean"] > 0 else float("inf")
print(f"\nImprovement factor (old_mean_err / new_mean_err) = {improvement_factor:.3f}")

-------------------------

Fused grid simulation + comparison harness

-------------------------

def run_fused_simulation(p: Dict[str, Any], use_evolved: bool, verbose: bool, dx: float) -> Tuple[Dict[str, np.ndarray], float, bool]:
rng = rng_default(p["seed"])
Nx = int(p["grid_Nx"])
x = np.linspace(-p["Lx"], p["Lx"], Nx)
state = 0.1 + 0.01 * np.exp(-0.5 * (x / 0.2) ** 2)
V = p["v0"] * (1.0 + p["alpha_s"] * state)
N_expect = 0.02
E_heat = 0.0
Emicro0 = float(N_expect)
Emacro0 = energy_of_state(state, dx)
E0 = Emicro0 + Emacro0 + E_heat
dt = float(p["dt_micro"])
times = np.arange(0.0, float(p["T_total"]), dt)
steps = len(times)
macrosync = max(1, int(round(p["dt_macro"] / dt)))
proj_kernel = np.exp(-0.5 * (x / 0.1) ** 2)
proj_kernel /= proj_kernel.sum()
coll_kernel = np.exp(-0.5 * (x / p["sigma_fus"]) ** 2)
coll_kernel /= coll_kernel.sum()
hist = {
"t": np.zeros(steps), "N_expect": np.zeros(steps), "E_micro": np.zeros(steps),
"E_macro": np.zeros(steps), "E_heat": np.zeros(steps), "Z_proxy": np.zeros(steps),
"S_center": np.zeros(steps), "trit_state": np.zeros(steps), "inj_adapt": np.zeros(steps),
"curvature": np.zeros(steps), "carbon_strength": np.zeros(steps)
}
fused_op = create_evolved_best_operator(dx) if use_evolved else None
carbon_synth = ZSF_CARBON_SYNTHESIS_grid(dx=dx, debug=args.debug)
injection_source = carbon_synth["injection_source"]
start_time = time.time()
failed = False
for k, t in enumerate(times):
xi_t = xi_of(t)
dN_drive = 1e-2 * xi_t * (1.0 - np.clip(N_expect, 0.0, 1.0)) * dt
dN_diss = -p["gammak"] * N_expect * dt
noise = rng.normal(scale=math.sqrt(max(p["sigma_noise"], 0.0)) * math.sqrt(dt))
N_expect = max(0.0, N_expect + dN_drive + dN_diss + noise)
N_collected = p["kappa_proj"] * N_expect
N_residual = max(0.0, N_expect - N_collected)
samples = sample_collision_residual(N_residual, p["Nmcsample"], rng)
if samples.size > 0:
deltainavg = float((p["eta_in"] * samples).mean())
deltaheatavg = float((p["eta_heat"] * samples).mean())
deltafusavg = float((p["eta_fus"] * samples).mean())
cap = p["maxinjectionfrac"] * max(1e-12, N_expect)
deltainavg = float(np.clip(deltainavg, -1e-3, min(1e-3, cap)))
deltaheatavg = float(np.clip(deltaheatavg, -1e-3, min(1e-3, cap)))
deltafusavg = float(np.clip(deltafusavg, -1e-3, min(1e-3, cap)))
else:
deltainavg = deltaheatavg = deltafusavg = 0.0
Gtot_scalar = N_collected + deltainavg + float(np.mean(injection_source))
Gtot_scalar = min(Gtot_scalar, p["maxGtotscalar"])
G_proj = Gtot_scalar * proj_kernel
G_fus = (1.0 - p["eta_fus"]) * deltafusavg * coll_kernel
G_total = G_proj + G_fus
if (k % macrosync) == 0:
if use_evolved and fused_op is not None:
state, info = fused_op.apply(state, rng)
hist["inj_adapt"][k] = float(info.get("inj_adapt", 0.0))
hist["trit_state"][k] = int(info.get("trit", 0))
else:
# Original upwind + diffusion + source
F = V * state
F_im = np.roll(F, 1)
convterm = -(macrosync * dt / dx) * (F - F_im)
diffterm = p["nu"] * (macrosync * dt / (dx * dx)) * laplacian_1d(state, dx)
srcterm = (macrosync * dt) * (G_total - p["gamma_env"] * state)
noise_term = rng.normal(scale=args.sigma_noise if not args.high_noise else args.sigma_noise * 5.0, size=state.shape)
state = state + convterm + diffterm + srcterm + noise_term
state = 0.995 * state + 0.005 * np.roll(state, 1)
if args.cfl_max > 0.0:
try:
dt = adjust_dt_by_cfl_1d(V, dx, dt, args.cfl_max, 1e-10, args.dt_micro * 10)
except Exception:
dt = max(1e-10, dt * 0.5)
if k % 100 == 0 and k > 0:
zs_trigger = {"state": state, "xi": xi_t, "lambda": 0.12, "phi": float(np.mean(state)),
"trit_state": int(hist["trit_state"][k]), "entropy": 0.0, "energy": float(hist["E_macro"][k-1])}
zs_out, trigger_log = DARKMATTER_TRIGGER_grid(zs_trigger, coupling_section=2e-40, Veff=1.0, rng=rng, dx=dx)
state = zs_out["state"]
hist["curvature"][k] = float(zs_out.get("curvature", 0.0))
if args.debug:
for msg in trigger_log:
print(f"[grid trigger] {msg}")
V = p["v0"] * (1.0 + p["alpha_s"] * state)
maxallowed2 = max(p["maxinjectionfrac"] * max(1e-12, N_expect), p["min_denominator"])
heatincrement = min(deltaheatavg, maxallowed2)
newmicromass = min(deltafusavg, maxallowed2)
E_heat += heatincrement
N_expect = max(0.0, N_expect - heatincrement + newmicromass)
Emicro = float(N_expect)
Emacro = energy_of_state(state, dx)
Etotal = Emicro + Emacro + E_heat
Zproxy = abs(Etotal - E0) / max(p["min_denominator"], abs(E0))
if Zproxy > p["Z_threshold"]:
deltaE = Etotal - E0
maxabsorb = p.get("maxabsorbenergy_frac", args.maxabsorbenergy_frac) * max(abs(E0), p["min_denominator"])
absorb = math.copysign(min(abs(deltaE), maxabsorb), deltaE)
E_heat -= absorb
state = state * p["safetydamp_coef"]
N_expect = N_expect * p["safetydamp_coef"]
hist["t"][k] = t
hist["N_expect"][k] = N_expect
hist["E_micro"][k] = Emicro
hist["E_macro"][k] = Emacro
hist["E_heat"][k] = E_heat
hist["Z_proxy"][k] = Zproxy
hist["S_center"][k] = float(state[len(state)//2])
hist["carbon_strength"][k] = float(carbon_synth["strengthGPa"])
if not np.isfinite(Etotal) or np.isnan(Zproxy) or np.any(~np.isfinite(state)):
failed = True
break
runtime = time.time() - start_time
if verbose:
print(f"Simulation finished: failed={failed}, runtime={runtime:.3f}s, final_trit={int(hist['trit_state'][-1]) if steps>0 else 0}")
return hist, runtime, failed

def run_fused_comparison(nruns: int):
seeds = [int(args.seed) + i for i in range(nruns)]
results = {"evolved": [], "original": []}
for use_evolved in [True, False]:
label = "evolved" if use_evolved else "original"
for s in seeds:
p = {
"seed": s, "grid_Nx": args.grid_Nx, "Lx": args.Lx, "T_total": args.T_total,
"dt_micro": args.dt_micro, "dt_macro": args.dt_macro, "v0": args.v0, "alpha_s": args.alpha_s,
"nu": args.nu, "gamma_env": args.gamma_env, "kappa_proj": args.kappa_proj, "eta_in": 0.05,
"eta_heat": 0.10, "eta_fus": 0.05, "sigma_fus": 0.03, "Nmcsample": args.Nmcsample,
"gammak": 5e-4, "sigma_noise": args.sigma_noise, "Z_threshold": args.Z_threshold,
"safetydamp_coef": args.safetydamp_coef, "maxGtotscalar": args.maxGtotscalar,
"min_denominator": args.min_denominator, "maxinjectionfrac": args.maxinjectionfrac,
"dt_macro": args.dt_macro
}
dx = 2.0 * p["Lx"] / max(1, p["grid_Nx"] - 1)
hist, runtime, failed = run_fused_simulation(p, use_evolved=use_evolved, verbose=True, dx=dx)
E_total = np.array(hist["E_micro"]) + np.array(hist["E_macro"]) + np.array(hist["E_heat"])
E0 = float(E_total[0])
err_E = np.abs(E_total - E0) / max(1e-12, abs(E0))
mean_err = float(np.mean(err_E))
max_err = float(np.max(err_E))
drift = float((E_total[-1] - E0) / max(1e-12, abs(E0)))
trit_active_rate = float(np.sum(np.array(hist["trit_state"]) != 0) / len(hist["trit_state"]))
results[label].append({
"mean_err": mean_err, "max_err": max_err, "drift": drift, "nan": failed,
"runtime": runtime, "trit_active_rate": trit_active_rate,
"final_carbon_strength": float(hist["carbon_strength"][-1]) if len(hist["carbon_strength"]) > 0 else 0.0
})
print(f"\n{label} seed={s}:")
print(f" mean_err={mean_err:.3e}, max_err={max_err:.3e}, drift={drift:.3e}")
print(f" trit_active_rate={trit_active_rate:.2f}, final_carbon_strength={hist['carbon_strength'][-1]:.2f}GPa")
print(f" nan={failed}, time={runtime:.3f}s")
def summarize(list_of_dicts: List[Dict[str, Any]]) -> Dict[str, float]:
mean_errs = [d["mean_err"] for d in list_of_dicts]
drifts = [d["drift"] for d in list_of_dicts]
trit_rates = [d["trit_active_rate"] for d in list_of_dicts]
carb_strs = [d["final_carbon_strength"] for d in list_of_dicts]
nans = sum(1 for d in list_of_dicts if d["nan"])
times = [d["runtime"] for d in list_of_dicts]
return {
"mean_err_mean": mean(mean_errs), "mean_err_std": stdev(mean_errs) if len(mean_errs) > 1 else 0.0,
"drift_mean": mean(drifts), "drift_std": stdev(drifts) if len(drifts) > 1 else 0.0,
"trit_rate_mean": mean(trit_rates), "carbon_strength_mean": mean(carb_strs),
"nan_count": nans, "time_mean": mean(times)
}
sum_e = summarize(results["evolved"])
sum_o = summarize(results["original"])
print("\n" + "="*50)
print("Fused Comparison Summary (Evolved vs Original)")
print("="*50)
print(f"Mean Energy Error: {sum_e['mean_err_mean']:.3e} ± {sum_e['mean_err_std']:.3e} vs {sum_o['mean_err_mean']:.3e} ± {sum_o['mean_err_std']:.3e}")
print(f"Energy Drift: {sum_e['drift_mean']:.3e} ± {sum_e['drift_std']:.3e} vs {sum_o['drift_mean']:.3e} ± {sum_o['drift_std']:.3e}")
print(f"Trit Active Rate: {sum_e['trit_rate_mean']:.2f} vs {sum_o['trit_rate_mean']:.2f}")
print(f"Final Carbon Strength (GPa): {sum_e['carbon_strength_mean']:.2f} vs {sum_o['carbon_strength_mean']:.2f}")
print(f"NaN Failures: {sum_e['nan_count']}/{nruns} vs {sum_o['nan_count']}/{nruns}")
print(f"Avg Runtime (s): {sum_e['time_mean']:.3f} vs {sum_o['time_mean']:.3f}")
improvement_factor = (sum_o["mean_err_mean"] / sum_e["mean_err_mean"]) if sum_e["mean_err_mean"] > 0 else float("inf")
print(f"\nEnergy Error Improvement Factor: {improvement_factor:.3f}x")

-------------------------

Main

-------------------------

def main():
print("=== zsfsynthesizedadaptive_fused: full-model demo (integrated all modules) ===")
print(f"injmult={args.inj_mult}, couplingmult={args.coupling_mult}, lowerthreshold={args.lower_threshold}, highnoise={args.high_noise}")
sources = [SOURCE_A, SOURCE_B, SOURCE_C]
mergeddefault = collidedict(SOURCE_A["params"], SOURCE_B["params"], RNG, exactcancel=args.exact_cancel)
mergeddefault = collidedict(mergeddefault, SOURCE_C["params"], RNG, exactcancel=args.exact_cancel)
mergeddefault["injectionamp"] = mergeddefault.get("injectionamp", 1e-4) * args.inj_mult
if "diffusionnu" in mergeddefault:
mergeddefault["diffusionnu"] = mergeddefault.get("diffusionnu", 1e-3) * args.coupling_mult
else:
mergeddefault["kappa"] = mergeddefault.get("kappa", 1e-3) * args.coupling_mult
mergeddefault["stabilize"] = True
mergeddefault["stabilize_params"] = {
"injectionamp": mergeddefault.get("injectionamp", 1e-4), "nearmargin": 0.12,
"mixgain": mergeddefault.get("mixgain", 5.0) * args.inj_mult,
"etaamp": mergeddefault.get("etaamp", 1e-5 if not args.high_noise else 5e-5),
"holdstrength": 0.12, "thetaplus": 0.35 if args.lower_threshold else 0.5,
"thetaminus": -0.35 if args.lower_threshold else -0.5
}
# Print sample params
samplekeys = ["xi","lambda","injectionamp","etaamp","mixgain","kappa","diffusionnu"]
sample = {k: mergeddefault.get(k) for k in samplekeys if k in mergeddefault}
print("Sample fused params:")
print(json.dumps(sample, indent=2))
# Single-point evolution demo
hist = unitpointevolutiondemo(Nsteps=args.steps, seed=12345, paramsoverride=mergeddefault)
for i in (0, max(0, args.steps // 4), args.steps - 1):
if i < len(hist["phi"]):
print(f"step {i}: phi={hist['phi'][i]:.6e}, trit={hist['trit'][i]}, injadapt={hist['injadapt'][i]:.6e}")
final_phi = hist["phi"][-1] if len(hist["phi"]) > 0 else None
final_trit = hist["trit"][-1] if len(hist["trit"]) > 0 else None
print("final phi:", final_phi)
print("final tritstate:", final_trit)
print()
# Trigger demo (single-point)
zstrigger = {"xi": 0.021, "lambda": 0.12, "phi": 0.0, "tritstate": 0, "entropy": 0.0,
"seed": 54321, "injectionadaptive": 0.0, "injprev": 0.0}
evo_params = {}
if args.high_noise:
evo_params["etaamp"] = 5e-5
if args.lower_threshold:
evo_params["thetaplus"] = 0.35
evo_params["thetaminus"] = -0.35
evo_params["stabilize"] = True
evo_params["stabilize_params"] = {
"injectionamp": evo_params.get("injectionamp", mergeddefault.get("injectionamp", 1e-4)),
"nearmargin": 0.12, "mixgain": mergeddefault.get("mixgain", 10.0),
"etaamp": evo_params.get("etaamp", mergeddefault.get("etaamp", 1e-5)),
"holdstrength": 0.12, "thetaplus": evo_params.get("thetaplus", 0.35), "thetaminus": evo_params.get("thetaminus", -0.35)
}
zsout, log = DARKMATTERTRIGGER(zstrigger, couplingsection=2e-40, Veff=1.0, rng=rng_default(54321), params={"evoparams": evo_params})
for m in log:
print(m)
print("after trigger zs:", {k: zsout.get(k) for k in ("phi","tritstate","entropy","energy","curvature")})
print()
# Carbon synthesis demo
res = ZSFCARBONSYNTHESISdemo(scale=None, clamp=False, autoscale=True, debug=args.debug)
print("carbon synthesis result:", res)
print()
# TRIT distribution
stats = unitpointevolutiondemo(Nsteps=2000, seed=42)
counts = np.bincount(np.array(stats["trit"]) + 1, minlength=3)
dist = counts / len(stats["trit"])
print("TRIT distribution (-1/0/1):", dist)
print()
# Candidate search (optional)
if args.search_candidates > 0:
print(f"Searching {args.search_candidates} candidates (grid_scaling=False to preserve single-point baseline)...")
cands = generatecandidates([SOURCE_A, SOURCE_B, SOURCE_C], RNG, args.search_candidates, exactcancel=args.exact_cancel, injmult=args.inj_mult, couplingmult=args.coupling_mult, grid_scaling=False)
val = validate_candidates(cands, RNG, steps=500, runs=4, debug=args.debug)
print("Candidate validation summary (top 6):")
for v in val[:6]:
print(v)
print()
# Regression test (optional)
rt = None
if args.regression_test:
print("Running regression test to verify single-point parity...")
rt = regression_test()
print("Regression result:", rt)
print()
# Original grid simulation (optional)
failed_grid = None
if args.use_grid:
print("Running original grid microcosm demo...")
p = copy.deepcopy(BASE_PARAMS)
p.update({"seed": args.seed})
hist_grid, runtime, failed = run_simulation(p, use_evolved=True, verbose=args.debug)
failed_grid = failed
print("Original grid demo runtime:", runtime, "failed:", failed)
print()
# Original grid comparison (optional)
if args.nruns > 0 and args.use_grid:
print("Running original grid comparison harness...")
run_comparison(nruns=args.nruns)
print()
# Fused grid comparison
print("=== Fused ZSF-Microcosm Model Comparison Test ===")
print(json.dumps({"seed": args.seed, "inj_mult": args.inj_mult, "grid_Nx": args.grid_Nx, "T_total": args.T_total, "nruns": args.nruns}, indent=2))
run_fused_comparison(nruns=args.nruns)
# Save a minimal result JSON
result_out = {
"single_point_final_phi": final_phi,
"single_point_final_trit": final_trit,
"carbon_strength_GPa": res["strengthGPa"],
"regression_test_passed": rt["trit_match"] if args.regression_test else None,
"original_grid_failed": failed_grid if args.use_grid else None
}
with open(args.result_save_path, "w", encoding="utf-8") as f:
json.dump(result_out, f, indent=2)
print("\n=== Simulation Completed ===")
print(f"Result saved to: {args.result_save_path}")

if name == "main":
main()