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Unitful
with DifferentialEquations.jl
? Preliminary benchmarks I've run show a performance hit of 10% or more which is a bit unfortunate.
LoadError: MethodError: no method matching TaylorN{Float64}(::Rational{Int64})
for both Tsit5()
and TaylorMethod
. As a work around, I looked into building the eom function and calling TaylorIntegration
directly, however the parsing performed in taylorinteg
is hitting a roadblock w/ RuntimeGeneratedFunctions
. I would prefer to used DiffEq directly but is there a simple stop-gap workaround for this. e.g. produce a function
instead of RuntimeGeneratedFunctions
?
[slack] <Adam Gerlach> ```using TaylorSeries, OrdinaryDiffEq, TaylorIntegration
function vanderpol!(du,u,ps,t)
du[1] = u[2]
du[2] = ps[1](1-u[1]^2)u[2]-u[1]
nothing
end
tspan = (0.0, 7.0)
u0 = [1.4, 2.4]
ps = [1.0]
prob = ODEProblem(vanderpol!, u0, tspan, ps)
solve(prob, Tsit5()) # success
solve(prob, TaylorMethod(20)) #success
ξ = set_variables("ξ", numvars=2, order=20)# \xi
u0ξ = u0.+ξ
prob2 = ODEProblem(vanderpol!, u0ξ, tspan, ps)
solve(prob2, Tsit5()) #LoadError: MethodError: no method matching TaylorN{Float64}(::Rational{Int64})```
qoldinit
, q...
etc.
solve
?
solve(prob2, Tsit5();
qmin = Float64(OrdinaryDiffEq.qmin_default(alg)),
qoldinit = OrdinaryDiffEq.isadaptive(alg) ? Float64(1//10^4) : 0.0,
gamma = Float64(OrdinaryDiffEq.gamma_default(alg)))
ERROR: LoadError: MethodError: no method matching TaylorN{Float64}(::Float64)
TaylorN
?
x ~ a + b + a
returns x ~ b + 2a
)which broke my tests. Does anyone one knows where this changes comes from? Is there any progress on the direct comparison of two equations or the total ordering of terms? There are some oldish issues about that (i.e. https://github.com/JuliaSymbolics/SymbolicUtils.jl/issues/85)…
[slack] <Donald Lee> ```using DifferentialEquations
using Sundials
using Plots
function lorenz!(du,u,p,t)
du[1] = 10.0(u[2]-u[1])
du[2] = u[1](28.0-u[3]) - u[2]
du[3] = u[1]u[2] - (8/3)u[3]
end
u0 = [1.0;0.0;0.0]
tspan = (0.0,100.0)
prob = ODEProblem(lorenz!,u0,tspan)
sol = solve(prob, CVODE_BDF())
plot(sol, vars=(0,1))
@. testfxn(t) = sol(t, idxs=1)
plot(0.0:0.1:100, testfxn)```
[slack] <ignace-computing> Hello diffeq enthusiasts,
I am looking for advice on accurate Finite Volume simulation packages/tools in Julia.
Specifically, it would be nice to simulate a 2D (stiff) time-dependent convection-diffusion problem.
Would you have any suggestions?
Thank you!