SciML/OrdinaryDiffEq.jl
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
What it solves
OrdinaryDiffEq.jl provides a comprehensive set of solvers and utilities for solving ordinary differential equations (ODEs). It allows researchers and engineers to model dynamic systems where the rate of change of a state variable is defined by a a set of equations, enabling the precise simulation of physical, biological, or mathematical models.
How it works
The library implements a wide variety of algorithms (such as Tsit5 and VelocityVerlet) to numerically integrate ODEs. Users define a problem using ODEProblem or SecondOrderODEProblem, specifying the initial conditions, time span, and the system of equations. The library supports both "out-of-place" syntax for simplicity and "in-place" syntax for higher efficiency in larger systems of equations. It also supports high-performance static arrays for model-specific compilation.
Who it’s for
It is designed for scientists, mathematicians, and engineers who need to solve differential equations as part of larger simulations or as a standalone tool for numerical analysis.
Highlights
- Diverse Solver Suite: Includes a broad range of ODE solvers, including symplectic integrators for Hamiltonian dynamics.
- Performance Optimization: Supports in-place updates and static arrays for maximum execution speed.
- High-Level Interface: Integrates with the broader SciML ecosystem for a consistent API across different equation types.
- Specialized Problem Types: Handles refined ODE forms such as second-order ODEs, IMEX, and semi-linear ODEs.
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