/PyTorchTutorial

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

Primary LanguagePython

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.

This is now alfonso's.

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