There are 5 repositories under surrogate-models topic.
Surrogate modeling and optimization for scientific machine learning (SciML)
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
Applications of PINOs
Sandia Uncertainty Quantification Toolkit
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
A step-by-step guide for surrogate optimization using Gaussian Process surrogate model
DrivAerNet: A Parametric Car Dataset for Data-driven Aerodynamic Design and Graph-Based Drag Prediction
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. :/
Efficient Multiscale Topology Optimization
An easy to use interface to gravitational wave surrogate models
Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions
LE-PDE accelerates PDEs' forward simulation and inverse optimization via latent global evolution, achieving significant speedup with SOTA accuracy
Python tool for creating Kriging surrogate models
Python interface to automatically formulate Machine Learning models into Mixed-Integer Programs
A python package for surrogate models that interface with calibration and other tools
Open-source constructor of surrogates and metamodels
This package contains a Rshiny webtool developed to allow the calculation of the metabolic predictors developed by the groups of MOLEPI and LCBC (LUMC), from raw Nightingale Health 1H-NMR metabolomics data.
Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
This is a small example project, that showcases the possibility of using a surrogate model to estimate the drag coefficient of arbitrary triangles.
A repository for solving heat equation in a rectangular fin with Physics Informed Neural Networks and Surrogate Models.
A demo of how to use a surrogate model for an optimization problem
This repository contains code for a project that trains a neural network to solve solid mechanics problems faster than the traditional finite element method. It includes a pipeline for generating a database of FEM solutions and experiments comparing the neural network model to the FEM.
Source code for Generative Adversarial Bayesian Optimization (GABO) for Surrogate Objectives