torch-molecule is a deep learning package for molecular discovery, designed with an sklearn-style interface for property prediction, inverse design and representation learning.

gRNAde is a Generative AI framework for inverse design of 3D RNA structure and function

This repository hosts a simple demonstration of a deep learning approach for the inverse design of patch antennas. The goal is to explore energy-efficient designs and to significantly reduce simulation cost compared to conventional methods.

Differentiable wave optics simulation library built on PyTorch

[NeurIPS 2020] Diversity-Guided Efficient Multi-Objective Optimization With Batch Evaluations

Optimization and inverse design of photonic crystals using deep reinforcement learning

A PyTorch Implementation of "Optimization of Molecules via Deep Reinforcement Learning".

Electromagnetics simulation library for moving point charges built on JAX

Efficient GPU-computing simulation for differentiable crystal plasticity finite element method

A collection of inverse design challenges

Silicon Photonics Design Tools.

A Generic Framework for Optical Inverse Design

AI-assisted Photonic Device Inverse Design Framework, MAPS DATE 2025

[ICLR24] CinDM uses compositional generative models to design boundaries and initial states significantly more complex than the ones seen in training for physical simulation

A Fortran-based neural network library for physics-based applications. Alongside standard neural network layer types, it also supports graph-based layers and physics informed neural networks.

GPU-accelerated electromagnetic FDTD simulations for compact modeling and inverse design / gradient-based optimization of nanophotonic devices with Python.

Differentiable Flexible Mechanical Metamaterials

PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design

Efficient GPU-computing simulation platform for differentiable phase field model

BC-Design: A Biochemistry-Aware Framework for High-Precision Inverse Protein Folding https://www.biorxiv.org/content/10.1101/2024.10.28.620755v2