JiTCSDE
swMATH ID:  24716 
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 justintimecompiled, allowing the user to efficiently integrate differential equations from a higherlevel 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/jitcsde 
Source Code:  https://github.com/neurophysik/jitcsde 
Dependencies:  Python 
Related Software:  JiTCODE; JiTCDDE; Ode15s; Conedy; PyDSTool; Matlab; dde23; PRMLT; EnKF; FiPy; LBFGS; CUPyDO; GitHub; NeuralPDE.jl; NetworkDynamicsBenchmarks; PackageCompiler.jl; StochasticDelayDiffEq.jl; StochasticDiffEq.jl; NetworkDynamics.jl; PowerDynamics.jl 
Cited in:  9 Publications 
Standard Articles
1 Publication describing the Software, including 1 Publication in zbMATH  Year 

Efficiently and easily integrating differential equations with JiTCODE, JiTCDDE, and JiTCSDE. Zbl 1390.34005 Ansmann, Gerrit 
2018

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Cited by 28 Authors
Cited in 5 Serials
5  Chaos 
1  Journal of Computational Physics 
1  Nonlinearity 
1  Engineering Analysis with Boundary Elements 
1  Understanding Complex Systems 
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