Scalable Identification of Physics-Based and Neural Dynamics from Partial Measurements
We present a scalable method for identifying nonlinear dynamics from partial measurements, supporting physics-based, neural-network, and gray-box models. We use the Prediction Error Method (PEM) coupled with state estimation performed by a Moving Horizon Estimation (MHE) scheme, which can account directly for general nonlinear dynamics. This results in a bi-level optimization problem that scales poorly with increasing model parameters. To improve computational efficiency, we derive a reformulation that avoids explicit parameter-wide sensitivity matrices and exploits the temporal structure of the underlying optimization problem. Numerical comparisons show up to a $215\times$ speedup in hyp...
| Authors | Yibo Zhou, Johannes Köhler |
|---|---|
| Primary category | eess.SY |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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