ZZKnight

pytorch_geometric

Graph Neural Network Library for PyTorch

BayesianOptimization

A Python implementation of global optimization with gaussian processes.

torchdyn

A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods

transdim

Machine learning for transportation data imputation and prediction.

AI-Feynman

Physics-informed-DeepONets

HPM

Hidden physics models: Machine learning of nonlinear partial differential equations

prog_models

The NASA Prognostic Model Package is a Python framework focused on defining and building models for prognostics (computation of remaining useful life) of engineering systems, and provides a set of prognostics models for select components developed within this framework, suitable for use in prognostics applications for these components.

USNCCM15-Short-Course-Recent-Advances-in-Physics-Informed-Deep-Learning

DeepVIV

Deep Learning of Vortex Induced Vibrations

phygnn

physics-guided neural networks (phygnn)

Deep-Learning-for-Spatio-temporal-Prediction

prog_algs

The Prognostic Algorithm Package is a python framework for model-based prognostics (computation of remaining useful life) of engineering systems, and provides a set of algorithms for state estimation and prediction, including uncertainty propagation. The algorithms take as inputs prognostic models (from NASA's Prognostics Model Package), and perform estimation and prediction functions. The library allows the rapid development of prognostics solutions for given models of components and systems. Different algorithms can be easily swapped to do comparative studies and evaluations of different algorithms to select the best for the application at hand.

DeePyMoD

LSTM_ROM_Arxiv

A Deep Learning based Approach to Reduced Order Modeling for Turbulent Flow Control using LSTM Neural Networks. arXiv:1804.09269

RCESN_spatio_temporal

Spatio-temporal forecasting of Lorenz96 with RC-ESN, RNN-LSTM and ANN

Data_Driven_Symbolic_Regression

Interpretable machine learning (symbolic regression) using Genetic programming/Gene expression programming and Sparse regression used to identify physical process, numerical schemes, and LES subgrid scale (eddy viscosity) turbulence models.

BayesianModelUpdating

Tutorials and examples of advanced sampling methods for solving Bayesian Model Updating Problems

Deep_Plates

Physics-guided neural network framework for elastic plates

BayesianDifferentiableProgramming

NCM2018

Sonkyo-Benchmark

Sonkyo blade benchmark

S3d

Matlab code for Y. Yuan, et. al. Machine Discovery of Partial Differential Equations from Spatiotemporal Data

PDE-Identification-Features

Data-driven Identification of 2D Partial Differential Equations using Extracted Physical Features

PDElearning

Code repository for the paper "Learning partial differential equations for biological transport models from noisy spatiotemporal data"

IncorporatingUnmodeledDynamics

Code for the paper "Incorporating unmodeled dynamics into first-principles models through machine learning"

PDE-Learning-few-time-samples

PDE_Discovery_Weak_Formulation

Codes for papers presenting a weak formulation approach to PDE discovery

ACDS

SparseARD