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lstm-human-activity-recognition

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN (Deep Learning algo). Classifying the type of movement amongst six activity categories - Guillaume Chevalier

machine-learning-applied-to-cfd

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

mantaflow

mantaflow is an open-source framework targeted at fluid simulation research in Computer Graphics and Machine Learning.

ml-cfd-lecture

Lecture material for machine learning applied to computational fluid mechanics

ml-visuals

🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

models

Models and examples built with TensorFlow

mpm3d-f90

neuralpde.jl

Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

neurodiffeq

A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.

niftynet

An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy

nougat

Implementation of Nougat Neural Optical Understanding for Academic Documents

ns-fenics

Navier-Stokes solvers based on FEniCS

nsga-ii

This is a python implementation of NSGA-II algorithm. NSGA is a popular non-domination based genetic algorithm for multi-objective optimization. It is a very effective algorithm but has been generally criticized for its computational complexity, lack of elitism and for choosing the optimal parameter value for sharing parameter σshare. A modified ve

oasis

oneflow

LargeScale Multiphysics Scientific Simulation Environment-OneFLOW CFD

operator-learning

pandas-tutorial

适合初级到中级晋升者，有了体系之后就看熟练度了。

path-planning

Implementation for A* with stricted visited list, Lifelong Planning A* and D* Lite final version

pbdl-book

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

pdebench

PDEBench: An Extensive Benchmark for Scientific Machine Learning

pg-is-all-you-need

Policy Gradient is all you need! A step-by-step tutorial for well-known PG methods.

phiflow

A differentiable PDE solving framework for machine learning

physics-based-deep-learning

Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond

physics-informed-deep-learning-solid-and-fluid-mechanics

Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube problems and plane stress linear elasticity boundary value problems

pinn

Physics-informed neural networks package

pinn-elastodynamics

physics-informed neural network for elastodynamics problem

pinn-laminar-flow

Physics-informed neural network for solving fluid dynamics problems

pinn-pytorch

Deep learning library for solving differential equations on top of PyTorch.

pinnlearning

Implement PINN with high level APIs of TF2.0, including a solution of coupled PDEs with PINN

pinnpapers

Must-read Papers on Physics-Informed Neural Networks.