Surrogate Modeling Toolbox

A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.

DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

Distributed Asynchronous Hyperparameter Optimization better than HyperOpt. 比HyperOpt更强的分布式异步超参优化库。

Collection of Multi-Fidelity benchmark functions

[NeurIPS 2022] Supervising the Multi-Fidelity Race of Hyperparameter Configurations

This repository comprises Jupyter Notebooks that serve as supplementary material to the journal article titled "Review of Multifidelity Models." The notebooks contain Python-based implementations that demonstrate toy problems in the multifidelity domain.

A set of reactor design benchmark problems to evaluate high-dimensional, expensive, and potentially multi-fidelity optimisation algorithms.

Project source code and data for multi-fidelity machine learning strategy for flame model identification

Flexible Gaussian Process model with user friendly kernel and mean function construction inspired by STHENO.

A suite of codes for dynamic analysis of offshore slender structures

Just a notebook reproducing the Non-linear Autoregressive Gaussian Process (Perdikaris et al, 2017) using Tensorflow Probability

A Python-based toolbox of various methods in uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.

Vanishing Viscosity solution predicting Graph Neural Networks and Domain Decomposable Reduced Order Models based on the Discontinuous Galerking method applied to Friedrichs' systems

Multi-fidelity modeling of wind farm wakes based on a novel super-fidelity network

[NeurIPS 2022] Supervising the Multi-Fidelity Race of Hyperparameter Configurations

Code for the paper "Multi-Fidelity Best-Arm Identification" (NeurIPS 2022)

This repository contains research on multi-fidelity Bayesian optimization, that I have presented on the Physics Days 2022

[NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks.

Code for the paper "Optimal Multi-Fidelity Best-Arm Identification" (NeurIPS 2024)