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autoencoder-tensorflow-examples

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

autoencoders-tf2.0

TensorFlow 2.0 implementations of various autoencoders.

bornscat

python library to compute forward scattering with the Born and Rytov approximations

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.

cvnn

Library to help implement a complex-valued neural network (cvnn) using tensorflow as back-end

deepgreen

DeepGreen network written in Tensorflow 2

deeponet

Learning nonlinear operators via DeepONet

deeponet-fno

DeepONet & FNO (with practical extensions)

framing-unet

## Quick and naive implementation of framing U-net paper in Keras (tf backend)

geo-fno

Geometry-Aware Fourier Neural Operator (Geo-FNO)

gn4ip

This is a Python package developed to more easily implement the methods described in William Herzberg's PhD Dissertation (Marquette University)

gnn-bvp-solver

Source code for paper "Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks"

greenlearning

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

helmholtz-fno

Neural operator surrogates for electromagnetic inverse design

hp-vpinns

hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations

mhd_pino

Use PINOs to solve MHD equations

mionet

MIONet: Learning multiple-input operators via tensor product

multifidelity-deeponet

Multifidelity DeepONet

operator-learning

physics-informed-deeponets

physics-informed-neural-network-pinn-

Implementation of a Physics Informed Neural Network (PINN) written in Tensorflow v2, which is capable of solving Partial Differential Equations.

pinn

A Physics Informed Neural Network Implementation for Burgers Equation in Tensorflow 2.0

pinn_tf2

implements PINN in TensorFlow 2.

solving-full-wave-nonlinear-inverse-scattering-problems-with-back-propagation-scheme

This Matlab code is used to solve inverse scattering problem with convolutional neural network by BPS.

tl-deeponet

Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.

uge-st

uno

xpinns_tensorflow-2

XPINN code written in TensorFlow 2