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Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equat…
🧞The highly productive Julia web framework
Powerful convenience for Julia visualizations and data analysis
Exiting VIM is hard; sometimes we need to take drastic measures
Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
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 i…
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learni…
Fast, continuous interpolation of discrete datasets in Julia
Julia bindings for the Enzyme automatic differentiator
Julia implementation of QuantEcon routines
Metaprogramming tools for DataFrames
A data structure for mathematical optimization problems
Package to make C++ libraries available in Julia
forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs
Krylov methods for linear problems, eigenvalues, singular values and matrix functions
Reverse Mode Automatic Differentiation for Julia
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs…
Implementation of a language-level autograd compiler
High accuracy derivatives, estimated via numerical finite differences (formerly FDM.jl)