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SciML / DifferentialEquations.jl

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Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components

python r julia ode dde partial-differential-equations dynamical-systems differential-equations differentialequations sde pde dae spde stochastic-differential-equations delay-differential-equations stochastic-processes differential-algebraic-equations scientific-machine-learning neural-differential-equations sciml
  • Updated Mar 18, 2021
  • Julia

SciML / ModelingToolkit.jl

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Open

Sparsity pattern without calculating the Jacobian?

2
ChrisRackauckas
ChrisRackauckas commented Mar 11, 2021

@shashi could we easily get support for:

using OrdinaryDiffEq, ModelingToolkit

const N = 32
const xyd_brusselator = range(0,stop=1,length=N)
brusselator_f(x, y, t) = (((x-0.3)^2 + (y-0.6)^2) <= 0.1^2) * (t >= 1.1) * 5.
limit(a, N) = a == N+1 ? 1 : a == 0 ? N : a
function brusselator_2d_loop(du, u, p, t)
  A, B, alpha, dx = p
  alpha = alpha/dx^2
  @inbounds for I in Cartesia
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enhancement good first issue
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SciML / Sundials.jl

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Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner

julia clang ode ida differential-equations ordinary-differential-equations differentialequations dae sundials cvode sensitivity-analysis differential-algebraic-equations kinsol idas scientific-machine-learning hybrid-differential-equation arkode sundials-libraries sciml
  • Updated Feb 8, 2021
  • Julia

SciML / DiffEqBase.jl

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The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

ode dde partial-differential-equations differential-equations ordinary-differential-equations differentialequations sde pde dae stochastic-differential-equations delay-differential-equations differential-algebraic-equations neural-ode scientific-machine-learning neural-differential-equations sciml
  • Updated Apr 1, 2021
  • Julia

SciML / DiffEqBayes.jl

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Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

julia ode dde probabilistic-programming differential-equations stan ordinary-differential-equations sde dae stochastic-differential-equations neural-ode scientific-machine-learning neural-differential-equations scientific-ml scientific-ai sciml
  • Updated Mar 19, 2021
  • Julia

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