🦐 Electromagnetic Simulation + Automatic Differentiation

TCLB - Templated MPI+CUDA/CPU Lattice Boltzmann code

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

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

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

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

Differentiable interface to FEniCS for JAX

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.

Adjoint-based optimization and inverse design of photonic devices.

A shock-capturing adjoint solver for the compressible flow equations

Create animations, plots, and calculate summary statistics for MITgcm adjoint output

Python package for solving implicit heat conduction

Combinatorial algorithms in bioinformatics - Adjoint Graph

Compute the gradient of the log likelihood function from a Kalman filter using the adjoint method.

A library for high-level algorithmic differentiation, principally for models written using FEniCS or Firedrake.

Easy interoperability with other libraries through NumPy interface to FEniCS adjoint

1D Heat Equation Model Problem for Field Inversion and Machine Learning Demonstration