A library for high-level algorithmic differentiation
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Updated
May 24, 2024 - Python
A library for high-level algorithmic 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.
Julia interface to MITgcm
Workshop materials for training in scientific computing and scientific machine learning
DAFoam: Discrete Adjoint with OpenFOAM for High-fidelity Multidisciplinary Design Optimization
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.
Create animations, plots, and calculate summary statistics for MITgcm adjoint output
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
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