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Search results for julia algorithmic differentiation

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4 search results found

Optimization.jl ⭐ 625

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.

Integrals.jl ⭐ 191

A common interface for quadrature and numerical integration for the SciML scientific machine learning organization

Nbodysimulator.jl ⭐ 122

A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics

Implicitad.jl ⭐ 16

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.

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