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ana_cont-greenfunc-

Package for analytic continuation of many-body Green's functions

avenn

bert-pytorch

Google AI 2018 BERT pytorch implementation

conservative_pinns

We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation property of cPINN is obtained by enforcing the flux continuity in the strong form along the sub-domain interfaces.

ddm_rbf

Solution of the Laplace equation in 2D using Domain Decomposition Method and Radial Basis Functions

deep-learning-for-inverse-problems

This project hosts the code and datasets I used for Deep Learning course at Boston University. It aims to post-process the images the low quality images produced as a result of solving inverse problems in imaging (particularly Computed Tomography) and produce high-quality images.

deep-learning2image-processing

deep learning for image processing including classification and object-detection etc.

deeplm

DeepLM: Large-scale Nonlinear Least Squares on Deep Learning Frameworks using Stochastic Domain Decomposition (CVPR 2021)

deeponet

Learning nonlinear operators via DeepONet

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.

deeppriors

Demo on using deep signal priors for inverse problems

dinn

Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, and finding their unique parameters (e.g. death rate).

dnn2convid

dnn2missdata_eq

efficient-kan_lxa

An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).

fem-dt-navier-test

Numerical simulation tests on navier-stokes, using splitting scheme.

fmpinn

Solving a class of elliptic partial differential equations(PDEs) with multiple scales utilizing Fourier-based mixed physics informed neural networks(dubbed FMPINN), the solver of FMPINN is configured as a multi-scale deep neural networks.

fno

Fourier_neural_operator

fourier-neural-operator

fourier_neural_operator

Use Fourier transform to learn operators in differential equations.

fourier_nn_csd3

Experiments with FNO and its variants over MHD and Navier-Stokes Modelling

friedrich-learning

galerkin-transformer

[NeurIPS 2021] Galerkin Transformer: a linear attention without softmax

gapointnet

gcn

Implementation of Graph Convolutional Networks in TensorFlow

gf-net-learninggreen-

Code for paper "Learning Green's Functions of Linear Reaction-Diffusion Equations with Application to Fast Numerical Solver"

google-research

Google Research

greenfunc_leraning

Machine learning Green functions from perturbation theories and ED

greenfunction

Polynomial approximation of Green functions. Inner product space class.

greenlearning

Learning Green's functions of partial differential equations with deep learning.