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gradientpathologiespinns

hagen-poiseuille-flow-with-lstm

Tutorial on using LSTM to predict Hagen-Poiseuille flow

hfm

Hidden Fluid Mechanics

ib-pinn

iclr_naca_dataset_v0

In this repository, you will find the different python scripts to train the available models on the 2D incompressible steady-state RAND solutions over NACA airfoils proposed at the Geometrical and Topological Representation Learning Workshop at ICLR 2022.

idrlnet

IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

image-restoration-using-srcnn

Deploying the super-resolution convolution neural network (SRCNN) using Keras

long-time-integration-pi-deeponets

machine-learning-applied-to-cfd

Examples of how to use machine learning algorithms in computational fluid dynamics.

ml-rom_various_shapes

This repository contains the simple source codes of "Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes"

mst-unet

multistepnns

Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems

neuralode-rom

This repository contains the files used in the paper " Reduced-order Model for Fluid Flows via Neural Ordinary Differential Equations"

nmor

Deep learning framework for model reduction of dynamical systems

optimally-weighted-pinns

pbdl-book

Welcome to the Physics-based Deep Learning Book (v0.2)

pg_mfnn

Collaboration with P&G using MFNN

phycrnet

Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs

phygeonet

PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain

physics-informed-neural-networks-for-ac-optimal-power-flow

This repository contains the code for Physics-Informed Neural Network for AC Optimal Power Flow applications and the worst case guarantees

physicsinformedtokentransformer

Official repository for Physics Informed Token Transformer (PITT)

pi-unet_hsl-tfp

This is the implementation of the PI-UNet for HSL-TFP

picnnsr

Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels -- parametric forward SR and boundary inference

pinn-elastodynamics

physics-informed neural network for elastodynamics problem

pinn-tf2.1

pinnpapers

Must-read Papers on Physics-Informed Neural Networks.

pinns

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

pinns_cylinder

pinns_rre

Physics-informed neural networks for the Richardson-Richards equation

pinnsntk