Skip to content

SciML/SteadyStateDiffEq.jl

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

SteadyStateDiffEq.jl

Join the chat at https://julialang.zulipchat.com #sciml-bridged Build Status Coverage Status codecov.io

SteadyStateDiffEq.jl is a component package in the DifferentialEquations ecosystem. It holds the steady state solvers for differential equations. While completely independent and usable on its own, users interested in using this functionality should check out DifferentialEquations.jl.

Usage

SteadyStateDiffEq.jl provides two main algorithms for finding steady states:

SSRootfind - Nonlinear Solver Approach

Use a nonlinear solver to directly find the steady state:

using SteadyStateDiffEq, NonlinearSolve

function f!(du, u, p, t)
    du[1] = 2 - 2u[1]
    du[2] = u[1] - 4u[2]
end

u0 = zeros(2)
prob = SteadyStateProblem(f!, u0)
sol = solve(prob, SSRootfind())

DynamicSS - Time Evolution Approach

Evolve the system forward in time until derivatives approach zero:

using SteadyStateDiffEq, OrdinaryDiffEq

sol = solve(prob, DynamicSS(Tsit5()))

For more details, see the SciML documentation.

Breaking Changes in v2

  1. NLsolve.jl dependency has been dropped. SSRootfind requires a nonlinear solver to be specified.
  2. DynamicSS no longer stores abstol and reltol. To use separate tolerances for the odesolve and the termination, specify odesolve_kwargs in solve.
  3. The deprecated termination conditions are dropped, see NonlinearSolve.jl Docs for details on this.

About

Solvers for steady states in scientific machine learning (SciML)

Topics

Resources

License

Code of conduct

Contributing

Security policy

Stars

Watchers

Forks

Sponsor this project

 

Packages

No packages published

Contributors 26

Languages