Combinatorial algorithms in bioinformatics - Adjoint Graph
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Updated
Dec 12, 2016 - C++
Combinatorial algorithms in bioinformatics - Adjoint Graph
An adjointable cardiac mechanics data assimilator.
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
Easy interoperability with Automatic Differentiation libraries through NumPy interface to FEniCS adjoint
1D Heat Equation Model Problem for Field Inversion and Machine Learning Demonstration
A shock-capturing adjoint solver for the compressible flow equations
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.
Goal Oriented Adaptive Lagrangian Mechanics
Python package for solving implicit heat conduction
Adjoint-based optimization and inverse design of photonic devices.
Automatic differentiation of FEniCS and Firedrake models in Julia
Reverse-mode automatic differentiation with delimited continuations
Goal-oriented error estimation and mesh adaptation for finite element problems solved using Firedrake
Differentiable interface to Firedrake for JAX
A Pytorch implementation of the radon operator and filtered backprojection with, except for a constant, adjoint radon operator and backprojection.
A library for high-level algorithmic differentiation
Julia interface to MITgcm
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