sdutta-math

PlotNeuralNet

Latex code for making neural networks diagrams

latent-diffusion

High-Resolution Image Synthesis with Latent Diffusion Models

pycaret

An open-source, low-code machine learning library in Python

deepxde

A library for scientific machine learning and physics-informed learning

PySR

High-Performance Symbolic Regression in Python and Julia

score_sde_pytorch

PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

PhiFlow

A differentiable PDE solving framework for machine learning

awesome-neural-ode

A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.

PyDMD

Python Dynamic Mode Decomposition

deeptime

Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation

deeponet

Learning nonlinear operators via DeepONet

ngsolve

Netgen/NGSolve is a high performance multiphysics finite element software. It is widely used to analyze models from solid mechanics, fluid dynamics and electromagnetics. Due to its flexible Python interface new physical equations and solution algorithms can be implemented easily.

Coursera-Deep-Learning

My notes / works on deep learning from Coursera

DeepKoopman

neural networks to learn Koopman eigenfunctions

PythonFOAM

In-situ data analyses and machine learning with OpenFOAM and Python

neuraluq

hpinn

hPINN: Physics-informed neural networks with hard constraints

turbulence-modeling-PIML

Data-driven Reynolds stress modeling with physics-informed machine learning

pysensors

PySensors is a Python package for sparse sensor placement

pinns-tf2

PINNs-TF2, Physics-informed Neural Networks (PINNs) implemented in TensorFlow V2.

Physics-constrained-Bayesian-deep-learning

Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data

HydroLSTM

LSTM Data and Model for Streamflow Forecasting

NPJCOMPUMATS-03747

Learning two-phase microstructure evolution using neural operators and autoencoder architectures

pinns-jax

PINNs-JAX, Physics-informed Neural Networks (PINNs) implemented in JAX.

PICNNSR

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

euler2d

EOR

DFEM-MMOC based MATLAB code for surfactant polymer flooding code - 2020Version 1