adjoint

🦐 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

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

TCLB - Templated MPI+CUDA/CPU Lattice Boltzmann code

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

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

Differentiable interface to FEniCS for JAX

Workshop materials for training in scientific computing and scientific machine learning

Differentiable interface to FEniCS for PyMC3

Reverse-mode automatic differentiation with delimited continuations

Automatic differentiation of FEniCS and Firedrake models in Julia

Differentiable interface to Firedrake for JAX

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

A library for high-level algorithmic differentiation, primarily for use with FEniCS or Firedrake

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

Adjoint-based optimization and inverse design of photonic devices.

Python package for solving implicit heat conduction

Goal Oriented Adaptive Lagrangian Mechanics

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.