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In: P294 - INFORMATIK 2019 - 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, Gesellschaft für Informatik e.V.

Priors for Linear Differential Equations

Dec 2019

We algorithmically construct multi-output Gaussian process priors which satisfy linear differential equations. We parametrize all solutions of the differential equations using Gröbner bases for controllable systems. If successful, a push forward along the parametrization is the desired prior. This prior yields an interpretable machine learning model, which can combine linear differential equations with noisy data points.

 

Literatur Beschaffung: P294 - INFORMATIK 2019 - 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, Gesellschaft für Informatik e.V.
@inproceedings{2379,
author= {Lange-Hegermann, Markus},
title= {Priors for Linear Differential Equations},
abstract= {We algorithmically construct multi-output Gaussian process priors which satisfy linear differential equations. We parametrize all solutions of the differential equations using Gröbner bases for controllable systems. If successful, a push forward along the parametrization is the desired prior. This prior yields an interpretable machine learning model, which can combine linear differential equations with noisy data points. },
booktitle= {P294 - INFORMATIK 2019 - 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft},
year= {2019},
month= {Dec},
publisher= {Gesellschaft für Informatik e.V.},
address= {},
editor= {},
pages= {},
organisation= {},
}

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