Training Neural Network potentials through customizable routines in JAX.

Distributed Asynchronous Hyperparameter Optimization in Python

Deep reinforcement learning to perform shape optimization

Learning in infinite dimension with neural operators.

北航研究生学位论文模板（Word+LaTeX）.

Learning Continuous Signed Distance Functions for Shape Representation

Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems

Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.

Must-read papers on graph neural networks (GNN)

TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.

This AI Smart Speaker uses speech recognition, TTS (text-to-speech), and STT (speech-to-text) to enable voice and vision-driven conversations, with additional web search capabilities via OpenAI and…

Have a conversation with ChatGPT using your voice, and have it talk back.

PDEBench: An Extensive Benchmark for Scientific Machine Learning

All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

A differentiable PDE solving framework for machine learning

Learning nonlinear operators via DeepONet

北京航空航天大学 数理统计 课程学习

IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by J. Nathan Kutz and Steven L. Brunton

Numerical integration in arbitrary dimensions on the GPU using PyTorch / TF / JAX

Calculate the curvature of discrete points

Using Long Short-Term Memory (LSTM) models to model the dynamics of Shape Memory Alloys (SMAs).

Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]

hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations