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arXiv · 2109.06786

Multiple shooting for training neural differential equations on time series

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.

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Evren Mert Turan, Johannes Jäschke. 2021-09-14. Multiple shooting for training neural differential equations on time series. https://doi.org/10.1109/lcsys.2021.3135835

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