DAFoam: Discrete Adjoint with OpenFOAM for High-fidelity Multidisciplinary Design Optimization
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Updated
Apr 14, 2024 - C
DAFoam: Discrete Adjoint with OpenFOAM for High-fidelity Multidisciplinary Design Optimization
🦐 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, SDEs, DDEs, DAEs, etc.
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
Adjoint-based optimization and inverse design of photonic devices.
Differentiable interface to FEniCS for JAX
Julia interface to MITgcm
Workshop materials for training in scientific computing and scientific machine learning
Goal-oriented error estimation and mesh adaptation for finite element problems solved using Firedrake
A library for high-level algorithmic differentiation
Python package for solving implicit heat conduction
Goal Oriented Adaptive Lagrangian Mechanics
Compute the gradient of the log likelihood function from a Kalman filter using the adjoint method.
Create animations, plots, and calculate summary statistics for MITgcm adjoint output
Reverse-mode automatic differentiation with delimited continuations
1D Heat Equation Model Problem for Field Inversion and Machine Learning Demonstration
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