swMATH ID: 24715
Software Authors: Ansmann, Gerrit
Description: Efficiently and easily integrating differential equations with JiTCODE, JiTCDDE, and JiTCSDE. We present a family of Python modules for the numerical integration of ordinary, delay, or stochastic differential equations. The key features are that the user enters the derivative symbolically and it is just-in-time-compiled, allowing the user to efficiently integrate differential equations from a higher-level interpreted language. The presented modules are particularly suited for large systems of differential equations such as those used to describe dynamics on complex networks. Through the selected method of input, the presented modules also allow almost complete automatization of the process of estimating regular as well as transversal Lyapunov exponents for ordinary and delay differential equations. We conceptually discuss the modules’ design, analyze their performance, and demonstrate their capabilities by application to timely problems.{parcopyright 2018 American Institute of Physics}
Homepage: https://github.com/neurophysik/jitcode
Source Code:  https://github.com/neurophysik/jitcode
Related Software: JiTCSDE; JiTCDDE; StochasticDiffEq.jl; Julia; Matlab; Python; Ode15s; StochasticDelayDiffEq.jl; NetworkDynamics.jl; PowerDynamics.jl; GitHub; LightGraphs.jl; DifferentialEquations.jl; Conedy; PyDSTool; SciPy; dde23; FiPy; L-BFGS; PRMLT
Cited in: 11 Documents

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