Taylor-mode automatic differentiation for higher-order derivatives
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Updated
May 31, 2024 - Julia
Taylor-mode automatic differentiation for higher-order derivatives
AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system.
forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs
An interface to various automatic differentiation backends in Julia.
Julia bindings for the Enzyme automatic differentiator
ODE integration using Taylor's method, and more, in Julia
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.
Automatic differentiation of implicit functions
Forward Mode Automatic Differentiation for Julia
Reverse Mode Automatic Differentiation for Julia
⟨Grassmann-Clifford-Hodge⟩ multilinear differential geometric algebra
Repository for automatic differentiation backend types
Taylor polynomial expansions in one and several independent variables.
Elliptic integrals and Jacobi elliptic functions that are GPU friendly
A package for binary and continuous, single and multi-material, truss and continuum, 2D and 3D topology optimization on unstructured meshes using automatic differentiation in Julia.
Julia interface for ColPack
Julia interface to the Generalised Truncated Power Series Algebra (GTPSA) library
An algebraic modeling and automatic differentiation tool in Julia Language, specialized for SIMD abstraction of nonlinear programs.
DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
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