A collection of codes to perform FWI with Devito

Scalable GPU seismic imaging toolkit for wave simulation, RTM, and FWI.

Rough Draft of Bike-Bench Repo to Accompany NeurIPS Submission

A list of projects that were or will be featured in Weekly Robotics newsletter

Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods

A collection of 100+ specialized Claude Code subagents covering a wide range of development use cases

Design engineering for Claude Code. Craft, memory, and enforcement for consistent UI.

DeepONet & FNO (with practical extensions)

A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data

[ICLR 2024] Scaling physics-informed hard constraints with mixture-of-experts.

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

Resources for UPT tutorials

PDEBench: An Extensive Benchmark for Scientific Machine Learning

A differentiable PDE solving framework for machine learning

Welcome to the Physics-based Deep Learning Book v0.3 - the GenAI Edition

Lightweight, general, scalable C++ library for finite element methods

Wave propagation modules for PyTorch.

🤖 Places where you can learn robotics (and stuff like that) online 🤖

A General Approach to Seismic Inversion Problems using Automatic Differentiation

Automatic Differentiation Library for Computational and Mathematical Engineering

A list of awesome open-source acoustic packages and resources.

An End-to-end Workflow for Adjoint Full Seismic Waveform Inversions

Elastic Full-Waveform Inversion Integrated with PyTorch

Full Waveform Inversion for Transmission Ultrasound Computed Tomography with Transmitting and Receiving Linear Array Transducers based on the Angular Spectrum Method

COIN-OR Interior Point Optimizer IPOPT

Official reproducible material for SiameseFWI: A Deep Learning Network for Enhanced Full Waveform Inversion

An Automatic Differentiation-based Waveform Inversion Framework Implemented in PyTorch.

PyTorch implementation of MoDL: Model Based Deep Learning Architecture for Inverse Problems

MoDL: Model-Based Deep Learning Architecture for Inverse Problems

Source code for our recent book entitled Model-Based Deep Learning