PriorIDENT: Prior-Informed PDE Identification from Noisy Data.

This work addresses the challenging problem of identifying governing partial differential equations (PDEs) from noisy spatiotemporal data. It highlights issues like differentiation-induced noise amplification and ambiguity from overcomplete libraries. The paper likely introduces a novel method, PriorIDENT, to overcome these challenges by incorporating prior information into the PDE identification process, with implications for data-driven discovery of physical laws.