Automatic differentiation (a.k.a algorithmic differentiation) in reverse mode for elm
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Updated
Nov 5, 2017 - Elm
Automatic differentiation (a.k.a algorithmic differentiation) in reverse mode for elm
Towards Sobolev Pruning (PASC'24 Conference Paper)
A simple, pure python algorithmic differentiation package
Demonstrator codes for MPI parallel taping and interpretation
Matrix derivative tests for algorithmic differentiation
msThesis
Differentiable Tensors based on NumPy Arrays
Mirror of bitbucket infergo-studies repository
Hyperelastic formulations using an algorithmic differentiation with hyper-dual numbers in Python.
mirror of Infergo repository
Material Definition with Automatic Differentiation
Algorithmic differentiation with hyper-dual numbers in C++ and Python
A library for high-level algorithmic differentiation
Various developer materials, like PDFs, notes, derivations, etc. for differential equations and scientific machine learning (SciML)
Automates adjoints. Forward and reverse mode algorithmic differentiation around implicit functions (not propagating AD through), as well as custom rules to allow for mixed-mode AD or calling external (non-AD compatible) functions within an AD chain.
Fast risks with QuantLib in C++
DRIP Fixed Income is a collection of Java libraries for Instrument/Trading Conventions, Treasury Futures/Options, Funding/Forward/Overnight Curves, Multi-Curve Construction/Valuation, Collateral Valuation and XVA Metric Generation, Calibration and Hedge Attributions, Statistical Curve Construction, Bond RV Metrics, Stochastic Evolution and Optio…
A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics
A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
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