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.
A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics
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…
Automates steady and unsteady adjoints (general solvers and ODEs respectively). 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++
Material Definition with Automatic Differentiation
A library for high-level algorithmic differentiation
Algorithmic differentiation with hyper-dual numbers in C++ and Python
mirror of Infergo repository
Various developer materials, like PDFs, notes, derivations, etc. for differential equations and scientific machine learning (SciML)
Differentiable Tensors based on NumPy Arrays
Hyperelastic formulations using an algorithmic differentiation with hyper-dual numbers in Python.
Matrix derivative tests for algorithmic differentiation
Towards Sobolev Pruning (PASC'24 Conference Paper)
A simple, pure python algorithmic differentiation package
Automatic differentiation (a.k.a algorithmic differentiation) in reverse mode for elm
Demonstrator codes for MPI parallel taping and interpretation
The works focus on pruning a neural network, structurally, based on the sensitivity of weights that cover the entire output range of the loss function..
This repository contains a software for performing optimized Dense Hessian Chain Bracketing.
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