The quest for nonlinear system identification models that are both physically interpretable and highly flexible remains a critical challenge. While classical methods offer structure at the cost of rigid parametric forms, and Neural ODEs provide expressiveness at the expense of transparency, a gap persists. Physics-Informed Neural Networks (PINNs) have bridged this divide, but their inverse variants falter when governing equations are unknown or state-dependent. Addressing this, the researchers introduce SOLIS, a framework that advances SOLIS nonlinear system identification by modeling unknown dynamics with a state-conditioned second-order surrogate model.
Unlocking Quasi-LPV Representations from Sparse Data
SOLIS fundamentally reframes system identification as learning a Quasi-Linear Parameter-Varying (Quasi-LPV) representation. This approach decouples trajectory reconstruction from parameter estimation, enabling the recovery of interpretable physical quantities such as natural frequency, damping, and gain without presupposing a global governing equation. This is particularly crucial for complex systems where the underlying dynamics are not readily apparent or change with the system's state. The framework has demonstrated accurate parameter-manifold recovery and coherent physical rollouts even from sparse datasets, succeeding in regimes where conventional inverse methods encounter identifiability failures.
