nguyenkhoa0209

deepchem

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

APMA_2070_ENGN_2912_SPRING_2024

pinns_tutorial

Tutorials for Physics-Informed Neural Networks

awesome-kan

A comprehensive collection of KAN(Kolmogorov-Arnold Network)-related resources, including libraries, projects, tutorials, papers, and more, for researchers and developers in the Kolmogorov-Arnold Network field.

rba-pinns

relative_balancing

DeepPDE-LidDrivenCavityFlow

The lid-driven cavity is a popular problem within the field of computational fluid dynamics (CFD) for validating computational methods. In this repository, we will walk through the process of generating a 2D flow simulation for the Lid Driven Cavity (LDC) flow using Nvidia Modulus framework.

pykan

Kolmogorov Arnold Networks

deepxde

A library for scientific machine learning and physics-informed learning

pinn-cm

Implementation of the PINN (Physic-Informed Neural Network) framework for continuum mechanics problems.

ml4physim_startingkit

LIPS

Burgers_1D

Solve the 1D forced Burgers equation with high order finite elements and finite difference schemes.

TensorFlow-2.x-Tutorials

TensorFlow 2.x version's Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT examples, etc. TF 2.0版入门实例代码，实战教程。

stanford-cs-229-machine-learning

VIP cheatsheets for Stanford's CS 229 Machine Learning

Physics-Informed-Neural-Networks

Investigating PINNs

PINNs

PyTorch Implementation of Physics-informed Neural Networks

xPINNs

when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/10.1016/j.cma.2022.115346

HMC-B-PINN---Jax-tutorial

B-PINN - Jax - HMC tutorial

Pole_projet_PINN

TF2 Implementation of Physics Informed Neural Networks and Neural Tangent Kernel

PINNacle

Codebase for PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.

PINNs_FBOAL

Fixed-Budget Online Adaptive Learning for PINNs

ROMHighContrast

We consider a parametric elliptic PDE with a scalar piecewise constant diffusion coefficient taking arbitrary positive values on fixed subdomains. This problem is not uniformly elliptic, as the contrast can be arbitrarily high, contrarily to the Uniform Ellipticity Assumption (UEA) that is commonly made on parametric elliptic PDEs. We construct reduced model spaces that approximate uniformly well all solutions with estimates in relative error that are independent of the contrast level. These estimates are sub-exponential in the reduced model dimension, yet exhibiting the curse of dimensionality as the number of subdomains grows. Similar estimates are obtained for the Galerkin projection, as well as for the state estimation and parameter estimation inverse problems. A key ingredient in our construction and analysis is the study of the convergence towards limit solutions of stiff problems when diffusion tends to infinity in certain domains.

Neural-PDE-Solver

mixedDEM

dem_hyperelasticity

A method based on a feed forward neural network to solve partial differential equations in nonlinear elasticity at finite strain based on the idea of minimum potential energy. The method is named "Deep Energy Method".

Deep_Plates

Physics-guided neural network framework for elastic plates

DEM_for_J2_plasticity

A deep energy method (DEM) to solve J2 elastoplasticity problems in 3D.

FBPINNs

Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)

PhysicsInformedPointNetElasticity

Implementation of Physics-Informed PointNet (PIPN) for weakly-supervised learning of 2D linear elasticity (plane stress) on multiple sets of irregular geometries