Encoding physics to learn reaction-diffusion processes
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Updated
Aug 28, 2023 - Python
Encoding physics to learn reaction-diffusion processes
A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
3D CNN to predict single-phase flow velocity fields
A framework based on the tensor train decomposition for working with multivariate functions and multidimensional arrays
Multi-fidelity Generative Deep Learning Turbulent Flows
Dimension reduced surrogate construction for parametric PDE maps
Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions
In this repository I publish the python code, that was part of my master thesis. The thesis can be found here, however its in German though, sry. :/
Python interface to automatically formulate Machine Learning models into Mixed-Integer Programs
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A python package for surrogate models that interface with calibration and other tools
Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
Open-source constructor of surrogates and metamodels
Physics-Informed Neural Network, Finite Element Method enhanced neural network, and FEM data-based neural network
Source code for Generative Adversarial Bayesian Optimization (GABO) for Surrogate Objectives
surF - a surrogate modeling method based on Discrete Fourier Transform
Code for AAAI 2024 paper: LRS: Enhancing Adversarial Transferability through Lipschitz Regularized Surrogate
Numerical Evidence for Sample Efficiency of Model-Based over Model-Free Reinforcement Learning Control of Partial Differential Equations [ECC'24]
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