Nathanael Bosch
I am a PhD student in machine learning at the University of Tübingen and the International Max Planck Research School for Intelligent Systems (IMPRS-IS), supervised by Prof. Dr. Philipp Hennig. I'm interested in probabilistic machine learning for and with dynamical systems, with a focus on probabilistic numerics: by treating numerical simulation as a probabilistic inference problem, we develop new methods that efficiently quantify their numerical error and enable new ways to do data-driven inference in dynamical systems.
Session
ProbNumDiffEq.jl implements probabilistic numerical ODE solvers for the DifferentialEquations.jl ecosystem. These methods intepret "solving an ODE" as a probabilistic inference task, and they return a probabilistic estimate of the numerical solution and approximation error. Under the hood, this is done with extended Kalman filtering - but to the user, it looks like any other DifferentialEquations.jl solver. This talk presents ProbNumDiffEq.jl, its functionality, and some implementation details.