Identifiability Guarantees for Drivers and Dynamics of Delayed Physical Systems

Abstract:A wide range of methods have been proposed, including physics-informed neural networks, which are powerful but do not guarantee identifiability of the dynamics, symbolic regression, which requires a set of precomputed operations, and causal discovery, which is more principled but usually relies on strong assumptions that physical systems may violate. In this work, we develop a theory-grounded method and prove that under a set of permissive assumptions, the structural drivers and drift of stochastic delayed differential equations are identifiable. Our method outperforms others on a benchmark for driver identifiability, and on a second benchmark to evaluate physical consistency of the learned dynamics.

Machine Learning (stat.ML); Machine Learning (cs.LG); Dynamical Systems (math.DS)

arXiv:2609.37944 [stat.ML]

(or arXiv:2609.37944v2 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2609.37944 Focus to learn more arXiv-issued DOI via DataCite

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