On a minimalistic interface for DifferentialEquations.jl

Specific Domains Numerics
1

I, like most people here, think that DifferentialEquations.jl is a truly fantastic package and one of the major reasons to use Julia over other langs. Still, I’ve found that it is often very bulky: instead of returning arrays, solutions to ODE problems have truly massive types like

EnsembleSolution Solution of length 1024 with uType:
RODESolution{Float64, 2, Vector{Vector{Float64}}, Nothing, Nothing, Vector{Float64}, NoiseProcess{Float64, 2, Float64, Vector{Float64}, Vector{Float64}, Vector{Vector{Float64}}, typeof(DiffEqNoiseProcess.WHITE_NOISE_DIST), typeof(DiffEqNoiseProcess.WHITE_NOISE_BRIDGE), Nothing, false, ResettableStacks.ResettableStack{Tuple{Float64, Vector{Float64}, Vector{Float64}}, false}, ResettableStacks.ResettableStack{Tuple{Float64, Vector{Float64}, Vector{Float64}}, false}, RSWM{Float64}, Nothing, RandomNumbers.Xorshifts.Xoroshiro128Plus}, Nothing, SDEProblem{Vector{Float64}, Tuple{Float64, Float64}, false, SciMLBase.NullParameters, Nothing, SDEFunction{false, SciMLBase.FullSpecialize, LorenzDrift, GeometricBrownianNoise, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, Nothing}, GeometricBrownianNoise, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, Nothing}, SRIW1, StochasticDiffEq.LinearInterpolationData{Vector{Vector{Float64}}, Vector{Float64}}, SciMLBase.DEStats, Nothing, Nothing}

Though likely useful for dispatch purposes, this often makes it hard to guarantee type stability and sanity when combining it with other packages. It also leads to comically large error messages whenever things break (as they often do in e.g., scientific machine learning); I am currently staring at a wall of text that is 120k characters long, the vast majority of which is mostly useless, inscrutable type information.

Is there an easy way to use the tooling of DifferentialEquations, but with more straightforward typing? I was hoping for something like scipy/torchdiffeq, where the solver takes in a function and an array of initial conditions and outputs an array of solutions.

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2

If you want just an array, you can call Array(sol) to turn it into a multidimensional array. There are some good reasons to not do this but if it simplifies your life then feel free to use it.

This is a missing Julia feature to cut non-dispatching type parameters out. @jeff.bezanson knows my qualms there and there’s some things for us to try.

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