Domain Adaptation with Hybrid Modeling for Learning Dynamical Systems

Akira Osaka, Naoya Takeishi, Takehisa Yairi

Abstract: Domain shifts present significant challenges for data-driven modeling of dynamical systems, as they may reduce state prediction accuracy and degrade model-based control performance. Transfer learning is a promising way to mitigate the effect of changes in dynamics. In this study, we investigate a domain adaptation framework based on hybrid modeling with fine-tuning. Hybrid models integrate physics-based components derived from prior knowledge of system dynamics into neural ordinary differential equations (neural ODEs). They are expected to facilitate efficient and enhanced fine-tuning because the structures of physics parts often remain invariant under domain shifts. We evaluated the hybrid neural ODE approach through experiments on multicopters undergoing concept shifts and found that introducing physics models significantly enhanced the domain adaptation capabilities, even when the physics-based components included unidentified parameters. Moreover, the results demonstrated that the hybrid modeling strategy reduced the amount of data required in the target domain, enabling efficient domain adaptation.

Submission Number: 167