Meta-Learning for Physically-Constrained Neural System Identification

TL;DR

This paper introduces a gradient-based meta-learning framework for quickly adapting neural state-space models to system identification tasks, incorporating physical constraints to enhance accuracy and generalizability.

Contribution

It proposes a novel meta-learning approach that leverages diverse source data and physical constraints for rapid, accurate system identification with limited target data.

Findings

Meta-learned models improve state estimation accuracy.

Incorporating physical constraints enhances model reliability.

Few online training iterations suffice for adaptation.

Abstract

We present a gradient-based meta-learning framework for rapid adaptation of neural state-space models (NSSMs) for black-box system identification. When applicable, we also incorporate domain-specific physical constraints to improve the accuracy of the NSSM. The major benefit of our approach is that instead of relying solely on data from a single target system, our framework utilizes data from a diverse set of source systems, enabling learning from limited target data, as well as with few online training iterations. Through benchmark examples, we demonstrate the potential of our approach, study the effect of fine-tuning subnetworks rather than full fine-tuning, and report real-world case studies to illustrate the practical application and generalizability of the approach to practical problems with physical-constraints. Specifically, we show that the meta-learned models result in improved downstream performance in model-based state estimation in indoor localization and energy systems.