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Poster
Fenrir: Physics-Enhanced Regression for Initial Value Problems
Filip Tronarp · Nathanael Bosch · Philipp Hennig

Tue Jul 19 03:30 PM -- 05:30 PM (PDT) @ Hall E #738
in Poster Session 1 »

We show how probabilistic numerics can be used to convert an initial value problem into a Gauss--Markov process parametrised by the dynamics of the initial value problem. Consequently, the often difficult problem of parameter estimation in ordinary differential equations is reduced to hyper-parameter estimation in Gauss--Markov regression, which tends to be considerably easier. The method's relation and benefits in comparison to classical numerical integration and gradient matching approaches is elucidated. In particular, the method can, in contrast to gradient matching, handle partial observations, and has certain routes for escaping local optima not available to classical numerical integration. Experimental results demonstrate that the method is on par or moderately better than competing approaches.

Keywords: PM: Bayesian Models and Methods · PM: Gaussian Processes
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Author Information

Filip Tronarp (University of Tübingen)
Nathanael Bosch (University of Tübingen)
Philipp Hennig (University of Tuebingen)

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