A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics
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Updated
May 6, 2024 - Julia
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
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.
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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