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, S...

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.

#计算机科学#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

#算法刷题#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.