A modeling framework for automatically parallelized scientific computing in Julia
216 Stars
Updated Last
10 Months Ago
Started In
February 2018


Build Status Coverage Status Stable Dev

ModelingToolkit.jl is a modeling framework for high-performance symbolic-numeric computation in scientific computing and scientific machine learning. It allows for users to give a high-level description of a model for symbolic preprocessing to analyze and enhance the model. ModelingToolkit can automatically generate fast functions for model components like Jacobians and Hessians, along with automatically sparsifying and parallelizing the computations. Automatic transformations, such as index reduction, can be applied to the model to make it easier for numerical solvers to handle.

For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation which contains the unreleased features.

High-Level Examples

First, let's define a second order riff on the Lorenz equations, symbolically lower it to a first order system, symbolically generate the Jacobian function for the numerical integrator, and solve it.

using ModelingToolkit, OrdinaryDiffEq

@parameters t σ ρ β
@variables x(t) y(t) z(t)
@derivatives D'~t

eqs = [D(D(x)) ~ σ*(y-x),
       D(y) ~ x*-z)-y,
       D(z) ~ x*y - β*z]

sys = ODESystem(eqs)
sys = ode_order_lowering(sys)

u0 = [D(x) => 2.0,
      x => 1.0,
      y => 0.0,
      z => 0.0]

p  ==> 28.0,
      ρ => 10.0,
      β => 8/3]

tspan = (0.0,100.0)
prob = ODEProblem(sys,u0,tspan,p,jac=true)
sol = solve(prob,Tsit5())
using Plots; plot(sol,vars=(x,y))


This automatically will have generated fast Jacobian functions, making it more optimized than directly building a function. In addition, we can then use ModelingToolkit to compose multiple ODE subsystems. Now, let's define two interacting Lorenz equations and simulate the resulting Differential-Algebraic Equation (DAE):

using ModelingToolkit, OrdinaryDiffEq

@parameters t σ ρ β
@variables x(t) y(t) z(t)
@derivatives D'~t

eqs = [D(x) ~ σ*(y-x),
       D(y) ~ x*-z)-y,
       D(z) ~ x*y - β*z]

lorenz1 = ODESystem(eqs,name=:lorenz1)
lorenz2 = ODESystem(eqs,name=:lorenz2)

@variables a
@parameters γ
connections = [0 ~ lorenz1.x + lorenz2.y + a*γ]
connected = ODESystem(connections,t,[a],[γ],systems=[lorenz1,lorenz2])

u0 = [lorenz1.x => 1.0,
      lorenz1.y => 0.0,
      lorenz1.z => 0.0,
      lorenz2.x => 0.0,
      lorenz2.y => 1.0,
      lorenz2.z => 0.0,
      a => 2.0]

p  = [lorenz1.σ => 10.0,
      lorenz1.ρ => 28.0,
      lorenz1.β => 8/3,
      lorenz2.σ => 10.0,
      lorenz2.ρ => 28.0,
      lorenz2.β => 8/3,
      γ => 2.0]

tspan = (0.0,100.0)
prob = ODEProblem(connected,u0,tspan,p)
sol = solve(prob,Rodas5())

using Plots; plot(sol,vars=(a,lorenz1.x,lorenz2.z))