tkf

module LyapunovExponentsWithForwardDiff

using DifferentialEquations
using ForwardDiff
using ParameterizedFunctions
using ProgressMeter
using RecipesBase

type LyapunovExponentsResult
    sol_p

ChrisRackauckas

Torchdiffeq vs DifferentialEquations.jl (/ DiffEqFlux.jl) Neural ODE Compatible Solver Benchmarks

Only non-stiff ODE solvers are tested since torchdiffeq does not have methods for stiff ODEs. The ODEs are chosen to be representative of models seen in physics and model-informed drug development (MIDD) studies (quantiative systems pharmacology) in order to capture the performance on realistic scenarios.

Summary

Below are the timings relative to the fastest method (lower is better). For approximately 1 million ODEs and less, torchdiffeq was more than an order of magnitude slower than DifferentialEquations.jl

IvanYashchuk

using PyFenicsAD
using Zygote
using PyCall
using Turing
import LinearAlgebra: norm
using Random, Distributions

fenics = pyimport("fenics")
fenics.set_log_level(fenics.LogLevel.ERROR)
fa = pyimport("fenics_adjoint")