Surrogate Modeling Toolbox
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Updated
Jul 5, 2024 - Jupyter Notebook
Surrogate Modeling Toolbox
DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
A set of reactor design benchmark problems to evaluate high-dimensional, expensive, and potentially multi-fidelity optimisation algorithms.
Code for the paper "Multi-Fidelity Best-Arm Identification" (NeurIPS 2022)
Just a notebook reproducing the Non-linear Autoregressive Gaussian Process (Perdikaris et al, 2017) using Tensorflow Probability
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.
Collection of Multi-Fidelity benchmark functions
Multi-fidelity modeling of wind farm wakes based on a novel super-fidelity network
This repository contains research on multi-fidelity Bayesian optimization, that I have presented on the Physics Days 2022
Distributed Asynchronous Hyperparameter Optimization better than HyperOpt. 比HyperOpt更强的分布式异步超参优化库。
A suite of codes for dynamic analysis of offshore slender structures
Project source code and data for multi-fidelity machine learning strategy for flame model identification
A Python-based toolbox of various methods in uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
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