🦐 Electromagnetic Simulation + Automatic Differentiation

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

DAFoam: Discrete Adjoint with OpenFOAM for High-fidelity Multidisciplinary Design Optimization

TCLB - Templated MPI+CUDA/CPU Lattice Boltzmann code

Frequency-domain photonic simulation and inverse design optimization for linear and nonlinear devices

A suite of photonic inverse design challenge problems for topology optimization benchmarking

Differentiable interface to FEniCS/Firedrake for JAX using dolfin-adjoint/pyadjoint

Differentiable interface to FEniCS for JAX

Workshop materials for training in scientific computing and scientific machine learning

Julia interface to MITgcm

Differentiable interface to FEniCS for PyMC3

A library for high-level algorithmic differentiation

A Pytorch implementation of the radon operator and filtered backprojection with, except for a constant, adjoint radon operator and backprojection.

Reverse-mode automatic differentiation with delimited continuations

Differentiable interface to Firedrake for JAX

Automatic differentiation of FEniCS and Firedrake models in Julia

Adjoint-based optimization and inverse design of photonic devices.

Goal-oriented error estimation and mesh adaptation for finite element problems solved using Firedrake

Python package for solving implicit heat conduction

Approximation algorithm to solve Optimal Control problems using the Adjoint Method. Assumes your controller is based on a parametric model. Uses Forward-Backward-Sweep adjoint method.