This package is deprecated and will not be maintained anymore, as the functionality of Fenrir.jl is now implemented in ProbNumDiffEq.jl. Just use ProbNumDiffEq.jl directy.
This package exports a single function, fenrir_nll:
fenrir_nll(prob::ODEProblem, data::NamedTuple{(:t, :u)}, observation_noise_var::Real,
diffusion_var::Union{Real,Vector{<:Real}};
adaptive=false, dt=false, proj=I, order=3::Int, tstops=[])
Compute the "Fenrir" approximate negative log-likelihood (NLL) of the data.
To see the full docstring, check out the documentation.
using ProbNumDiffEq, Plots, LinearAlgebra, Fenrir
# Define problem:
function f(du, u, p, t)
a, b, c = p
du[1] = c*(u[1] - u[1]^3/3 + u[2])
du[2] = -(1/c)*(u[1] - a - b*u[2])
end
u0 = [-1.0, 1.0]
tspan = (0.0, 20.0)
p = (0.2, 0.2, 3.0)
prob = ODEProblem(f, u0, tspan, p)
# Generate data:
true_sol = solve(prob, EK1())
times = 1:0.1:20
odedata = [u + 0.1*randn(size(u)) for u in true_sol(times).u.μ]
scatter(times, ProbNumDiffEq.stack(odedata), markersize=2, markerstrokewidth=0.1,
color=1, label=["Data" ""])
# With the wrong parameters:
pwrong = (0.1, 0.1, 2.0)
solwrong = solve(remake(prob, p=pwrong), EK1(smooth=false), dense=false);
plot!(solwrong, color=2, label=["Wrong solution" ""])
# Fenrir:
data = (t=times, u=odedata);
σ² = 1e-1
κ² = 1e30
nll, ts, states = fenrir_nll(remake(prob, p=pwrong), data, σ², κ²)
means = ProbNumDiffEq.stack([x.μ for x in states]);
stddevs = ProbNumDiffEq.stack([sqrt.(diag(x.Σ)) for x in states]);
plot!(ts, means, ribbon=2stddevs,
marker=:o, markersize=1, markerstrokewidth=0.1,
color=3, fillalpha=0.1, label=["Fenrir interpolation" ""])
println("Negative log-likelihood: $nll")
Prints: Negative log-likelihood: 5849.3096741464615
You can use this NLL it as any other NLL: Optimize it to compute maximum-likelihood estimates or MAPs, or plug it into MCMC to sample from the posterior. In our paper we compute MLEs by pairing Fenrir with Optimization.jl and ForwardDiff.jl. Check out the documentation for more details on how to do this.
This method has been developed in the paper "Fenrir: Physics-Enhanced Regression for Initial Value Problems" by Tronarp et al, published at ICML 2022 (link).
@InProceedings{pmlr-v162-tronarp22a,
title = {Fenrir: Physics-Enhanced Regression for Initial Value Problems},
author = {Tronarp, Filip and Bosch, Nathanael and Hennig, Philipp},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {21776--21794},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/tronarp22a/tronarp22a.pdf},
url = {https://proceedings.mlr.press/v162/tronarp22a.html}
}