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ar_pipeline

attention-is-all-you-need

characterizing-pinns-failure-modes

Characterizing possible failure modes in physics-informed neural networks.

cov-weighting

Implementation for our WACV 2021 paper "Multi-Loss Weighting with Coefficient of Variations"

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".

dolfinx_porous_media

Multi-compartment porous modeling using FEniCSx: from the theory to the implementation

eeg-atcnet

Attention temporal convolutional network for EEG-based motor imagery classification

fenics_tutorial

FEniCS tutorial prepared for the KITP Active Matter workshop (https://www.kitp.ucsb.edu/activities/active20)

fenicsx-fracture

FEniCSx Fracture mechanics examples

fenicsxcourse

florence

A finite element framework for Python's scientific stack: arbitrary order planar/curvilinear mesh generation and finite element methods for linear and nonlinear analysis of coupled multiphysics problems

fourier_neural_operator

Use Fourier transform to learn operators in differential equations.

graphgalerkin

Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems

hybrid-fem-nn-examples

hyperparameter-optimization

jax-am

Additive manufacturing simulation with JAX.

kratos

Kratos Multiphysics (A.K.A Kratos) is a framework for building parallel multi-disciplinary simulation software. Modularity, extensibility and HPC are the main objectives. Kratos has BSD license and is written in C++ with extensive Python interface.

mixeddem

mmtmtonn

parametric-deep-energy-method

pdebench

This is a repository for the paper "PDEBench: An Extensive Benchmark for Scientific Machine Learning".

physics-informed-deeponets

pinnsntk

plotneuralnet

Latex code for making neural networks diagrams

presentations

pytorch-cyclegan-and-pix2pix

Image-to-Image Translation in PyTorch

pytorch-fourier-feature-networks

Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains

pytorch-lbfgs

A PyTorch implementation of L-BFGS.

pytorch-minimize

Newton and Quasi-Newton optimization with PyTorch

random-fourier-features-pytorch

Implementation of "Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains" by Tancik et al.