Popular GitHub Repositories for Parametric Pdes

Unsupervised Feature Learning via Non-parametric Instance Discrimination

A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks

Graph Neural PDEs

PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks

Automatic Differentiation Library for Computational and Mathematical Engineering

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

This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs

Official code for Dynamic Parametric RAG.

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

Generative Pre-Trained Physics-Informed Neural Networks Implementation

?? Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatments from observational data using neural networks.

A Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionality

Clique recycling non-Gaussian (multi-modal) factor graph solver; also see Caesar.jl.

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

Here I will try to implement the solution of PDEs using PINN on pytorch for educational purpose

CPAB Transformations: finite-dimensional spaces of simple, fast, and highly-expressive diffeomorphisms derived from parametric, continuously-defined, velocity fields in Numpy, Tensorflow and Pytorch

pytorch implementation of Parametric Noise Injection for adversarial defense

Statistical inference on machine learning or general non-parametric models

Dimension reduced surrogate construction for parametric PDE maps

GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.