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SciML / DiffEqApproxFun.jl

The tools for proper interactions between ApproxFun.jl and DifferentialEquations.jl for pseudospectiral partial differential equation discretizations in scientific machine learning (SciML)
https://benchmarks.sciml.ai/
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Old requires overloads #17

Open ChrisRackauckas opened 2 years ago

ChrisRackauckas commented 2 years ago

For safe keeping:

from DiffEqBase.jl:

  @require ApproxFun="28f2ccd6-bb30-5033-b560-165f7b14dc2f" begin
    eval_u0(u0::ApproxFun.Fun) = false
  end

from RecursiveArrayTools.jl:

  @require ApproxFun="28f2ccd6-bb30-5033-b560-165f7b14dc2f" begin
    RecursiveArrayTools.recursive_unitless_eltype(a::ApproxFun.Fun) = typeof(a)
    RecursiveArrayTools.recursive_unitless_bottom_eltype(a::ApproxFun.Fun) = recursive_unitless_bottom_eltype(ApproxFun.coefficients(a))
    RecursiveArrayTools.recursive_bottom_eltype(a::ApproxFun.Fun) = recursive_bottom_eltype(ApproxFun.coefficients(a))
  end