Identifiability Guarantees for Drivers and Dynamics of Delayed Physical Systems
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 learn...
| Authors | Julien Boussard, Antoine Debouchage, Théo Saulus |
|---|---|
| Primary category | stat.ML |
| Published | 29 September 2026 |
| Last updated at source | 1 October 2026 |
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