ProbNumDiffEq.jl provides probabilistic ODE solvers for the DifferentialEquations.jl ecosystem.
The field of Probabilistic Numerics aims to quantify numerical uncertainty arising from finite computational resources. This package implements ODE filters, a class of probabilistic numerical methods for solving ordinary differential equations. By casting the solution of ODEs as a problem of Bayesian inference, they solve ODEs with methods of Bayesian filtering and smoothing. As a result, the solvers return a posterior probability distribution over ODE solutions and provide estimates of their own numerical approximation error.
The package can be installed directly with the Julia package manager:
] add ProbNumDiffEq
using ProbNumDiffEq # ODE definition as in DifferentialEquations.jl function fitz(u, p, t) a, b, c = p return [c*(u - u^3/3 + u) -(1/c)*(u - a - b*u)] end u0 = [-1.0; 1.0] tspan = (0., 20.) p = (0.2,0.2,3.0) prob = ODEProblem(fitz, u0, tspan, p) # Solve the ODE with a probabilistic numerical solver: EK0 sol = solve(prob, EK0(), abstol=1e-1, reltol=1e-2) # Plot the solution using Plots plot(sol, fillalpha=0.15) # Sample from the solution and plot the samples samples = ProbNumDiffEq.sample(sol, 100) for i in 1:100 plot!(sol.t, samples[:, :, i], color=[1 2], label="", linewidth=0.1) end