Combinatorial algorithms in bioinformatics - Adjoint Graph
-
Updated
Dec 12, 2016 - C++
Combinatorial algorithms in bioinformatics - Adjoint Graph
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.
Adjoint-based optimization and inverse design of photonic devices.
1D Heat Equation Model Problem for Field Inversion and Machine Learning Demonstration
Frequency-domain photonic simulation and inverse design optimization for linear and nonlinear devices
A shock-capturing adjoint solver for the compressible flow equations
Easy interoperability with Automatic Differentiation libraries through NumPy interface to FEniCS adjoint
Differentiable interface to Firedrake for JAX
Python package for solving implicit heat conduction
Automatic differentiation of FEniCS and Firedrake models in Julia
An adjointable cardiac mechanics data assimilator.
Compute the gradient of the log likelihood function from a Kalman filter using the adjoint method.
Differentiable interface to FEniCS for JAX
Goal Oriented Adaptive Lagrangian Mechanics
A Pytorch implementation of the radon operator and filtered backprojection with, except for a constant, adjoint radon operator and backprojection.
Reverse-mode automatic differentiation with delimited continuations
🦐 Electromagnetic Simulation + Automatic Differentiation
Differentiable interface to FEniCS/Firedrake for JAX using dolfin-adjoint/pyadjoint
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."