#scientific-machine-learning

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.

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

The Base interface of the SciML ecosystem

Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications

Reservoir computing utilities for scientific machine learning (SciML)

A general interface for symbolic indexing of SciML objects used in conjunction with Domain-Specific Languages