MinglangYin / PyTorchTutorial Public

Notifications You must be signed in to change notification settings
Fork 13
Star 40

Examplary code for NN, MFNN, DynNet, PINNs and CNN

40 stars 13 forks Branches Tags Activity

Name		Name	Last commit message	Last commit date
Latest commit History 26 Commits
CNN		CNN
DynNet		DynNet
Gradient		Gradient
MFNN		MFNN
NN		NN
PINNs		PINNs
Utils		Utils
.DS_Store		.DS_Store
README.md		README.md

Repository files navigation

PyTorch Tutorial for Physics-informed Machine Learning

This repository contains several key models in Physice-informed machine learning (PI-ML) and data-driven machine learning written in PyTorch.

Data-driven models

Neural Network (NN)
- Standard fully connected neural networks
Multi-fidelity Neural Network (MFNN)
- Three standard neural networks coupled to fit high-fidelity data, high-fidelity data and their linear combination.
Convolutional Neural Network (CNN)
- Convolutional neural network(Decoder)

Physics-informed Machine Learning (PIML)

Physical-informed Neural Networks (PINNs)
- Physical-informed neural network for solving partial differential equations, e.g., Allen-Cahn equation(1D time-dependent and 2D equilibrium state)
Deep Operator Networks (DeepONet)
- DeepONet for learning a PDE operator

Proof of Concept are listed below:

DynNet (Dynamic-graph Network)
- Fully-connected neural network to demonstrate the concept of dynamic graph.
Gradient (Automatic Differentiation)
- Calculate gradient in PyTorch