🦐 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
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
#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.