The field of Probabilistic Numerics aims to quantify numerical uncertainty arising from finite computational resources. By treating the solution of an ordinary differential equation (ODE) as a problem of Bayesian inference, probabilistic numerical ODE solvers return a posterior probability distribution over ODE solutions. This poster presents ProbNumDiffEq.jl, a package for probabilistic numerical solvers for ODEs, built on OrdinaryDiffEq.jl.
Nathanael Bosch
I'm a PhD student at University of Tübingen, working on probabilistic numerics, machine learning, and Bayesian inference.