SciML / OrdinaryDiffEq.jl

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
https://diffeq.sciml.ai/latest/
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Code stalls when adaptive time stepping is requested for a non-adaptive method. #2265

Open SKopecz opened 6 days ago

SKopecz commented 6 days ago

Describe the bug 🐞

When adaptive time stepping is explicitly required via adaptive = true in combination with a non-adatpive method, the code will stall.

Expected behavior

I would expect that an error is thrown, which states that adaptive = true cannot be used together with a non-adatpive method.

Minimal Reproducible Example 👇

using OrdinaryDiffEq
f(u, p, t) = 1.01 * u
u0 = 1 / 2
tspan = (0.0, 1.0)
prob = ODEProblem(f, u0, tspan);

solve(prob, Euler(); dt = 0.1, adaptive=true)

Environment (please complete the following information):

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Julia Version 1.10.0
Commit 3120989f39b (2023-12-25 18:01 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 8 × Intel(R) Core(TM) i7-8565U CPU @ 1.80GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, skylake)
  Threads: 1 on 8 virtual cores
ChrisRackauckas commented 4 days ago

Oh interesting, never thought of trying that 😅 . Yeah we should throw an early error about that. Putting that to @oscardssmith