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Computer Science > Machine Learning

arXiv:2109.06786 (cs)
[Submitted on 14 Sep 2021 (v1), last revised 17 Dec 2021 (this version, v2)]

Title:Multiple shooting for training neural differential equations on time series

Authors:Evren Mert Turan, Johannes Jäschke
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Abstract:Neural differential equations have recently emerged as a flexible data-driven/hybrid approach to model time-series data. This work experimentally demonstrates that if the data contains oscillations, then standard fitting of a neural differential equation may result in a flattened out trajectory that fails to describe the data. We then introduce the multiple shooting method and present successful demonstrations of this method for the fitting of a neural differential equation to two datasets (synthetic and experimental) that the standard approach fails to fit. Constraints introduced by multiple shooting can be satisfied using a penalty or augmented Lagrangian method.
Comments: in IEEE Control Systems Letters
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2109.06786 [cs.LG]
  (or arXiv:2109.06786v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.06786
Related DOI: https://doi.org/10.1109/LCSYS.2021.3135835

Submission history

From: Evren Turan [view email]
[v1] Tue, 14 Sep 2021 15:56:37 UTC (884 KB)
[v2] Fri, 17 Dec 2021 10:42:30 UTC (1,872 KB)
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