DiffEqBase.jl
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SciML
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
The fix in #784 resulted in extremely poor performance for Modia3D. ```julia using Modia3D include("$(Modia3D.path)/test/Basic/PendulumWithBar1.jl") ``` gives the following timings for DiffEqBase versions (at the second run): - version 6.92.2:...
As shown in https://github.com/SciML/DiffEqJump.jl/issues/239, a user might accidentally double include some `tstops` when using callbacks. Maybe it would make sense for `DiffEqBase.initialize!` to call `unique!` on the `tstops`?
Needs: - https://github.com/SciML/SciMLBase.jl/pull/143 - https://github.com/SciML/OrdinaryDiffEq.jl/pull/1627 ```julia using OrdinaryDiffEq function f(du, u, p, t) du[1] = 0.2u[1] du[2] = 0.4u[2] end u0 = ones(2) tspan = (0.0, 1.0) prob = ODEProblem{true,false}(f,...
This is a continuation of https://github.com/SciML/DiffEqBase.jl/pull/736 instead using OpaqueClosures. As such, it also requires: - https://github.com/SciML/SciMLBase.jl/pull/143 - https://github.com/SciML/OrdinaryDiffEq.jl/pull/1627 But now the MWE doesn't run: ```julia using OrdinaryDiffEq function f(du, u,...
Should it work like this? ```jl using OrdinaryDiffEq f(u,p,t) = 1.01*u u0 = 1/2 tspan = (0.0,1.0) prob = ODEProblem(f,u0,tspan) sol = solve(prob, Tsit5(), saveat=[0.0,0.0,0.5,0.5,1.0,1.0]) println(sol.t) [0.0, 0.5, 0.5, 1.0,...
When using Iterative solvers, it is very useful to have the log of convergence to attest the usefullness / efficiency of a preconditionner. The option is not present in the...
Hi! With the following code: ```julia module MyTest2 using ArrayInterface export A struct A{T} a::T end ArrayInterface.ismutable(::Type{
