A library for scientific machine learning and physics-informed learning

Identifying heterogeneous micromechanical properties of biological tissues via physics-informed neural networks

Here I will try to implement the solution of PDEs using PINN on pytorch for educational purpose

A Jupyter Notebook implementation of Physics-informed neural network to solve solid mechanics problem.

A pytorch implementation of WGAN-GP

The code is for implementing fracture behavior of micropolar continuum with peridynamic differential operator method.

A CNN Variational Autoencoder (CNN-VAE) implemented in PyTorch

For beginner, this will be the best start for VAEs, GANs, and CVAE-GAN. This contains AE, DAE, VAE, GAN, CGAN, DCGAN, WGAN, WGAN-GP, VAE-GAN, CVAE-GAN. All use PyTorch.

Python code for solving some 2D peridynamic problems

Matlab Peridynamics Code

A Backward Compatible -- Physics Informed Neural Network for Allen Cahn and Cahn Hilliard Equations

Numerical solutions of Cahn-Hilliard equation on various domains

METASET: Exploring Shape and Property Spaces for Data-Driven Metamaterials Design

IH-GAN, data generation, and topology optimization code associated with our accepted CMAME 2022 paper: "IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular …

Python codes for generating a dataset with topology optimization results for the base cell of a periodic metamaterial

Data and code for structure-property correlation in 2D Cellular Metamaterials

A computational building block approach towards multiscale architected materials analysis and design with application to hierarchical metal metamaterials

Code for 'Unifying the design space of truss metamaterials by generative modeling'

Implementation of 'Inverting the structure-property map of truss metamaterials via deep learning' (PNAS)

Inverse design of 3D truss networks with automatic differentiation

Here, we use a conditional deep convolutional generative adversarial network (cDCGAN) to inverse design across multiple classes of metasurfaces. Reference: https://onlinelibrary.wiley.com/doi/10.10…

Implementation of 'Inverse-design of nonlinear mechanical metamaterials via video denoising diffusion models' (Nature Machine Intelligence)

Conditional denoising diffusion probabilistic model trained in latent space.

A Hands-on Introduction to Physics-Informed Neural Networks

Physics-Informed Neural Networks for solving PDEs (bachelor project)