DAFoam: Discrete Adjoint with OpenFOAM for High-fidelity Multidisciplinary Design Optimization
-
Updated
Jul 3, 2024 - C
DAFoam: Discrete Adjoint with OpenFOAM for High-fidelity Multidisciplinary Design Optimization
Julia interface to MITgcm
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.
A library for high-level algorithmic differentiation
Create animations, plots, and calculate summary statistics for MITgcm adjoint output
Workshop materials for training in scientific computing and scientific machine learning
A suite of photonic inverse design challenge problems for topology optimization benchmarking
Goal-oriented error estimation and mesh adaptation for finite element problems solved using Firedrake
Differentiable interface to FEniCS/Firedrake for JAX using dolfin-adjoint/pyadjoint
🦐 Electromagnetic Simulation + Automatic Differentiation
Reverse-mode automatic differentiation with delimited continuations
A Pytorch implementation of the radon operator and filtered backprojection with, except for a constant, adjoint radon operator and backprojection.
Goal Oriented Adaptive Lagrangian Mechanics
Differentiable interface to FEniCS for JAX
Compute the gradient of the log likelihood function from a Kalman filter using the adjoint method.
An adjointable cardiac mechanics data assimilator.
Automatic differentiation of FEniCS and Firedrake models in Julia
Add a description, image, and links to the adjoint topic page so that developers can more easily learn about it.
To associate your repository with the adjoint topic, visit your repo's landing page and select "manage topics."