Sparsistent Model Discovery

Understand

Discovering the partial differential equations underlying spatio-temporal datasets from very limited and highly noisy observations is of paramount interest in many scientific fields.

• However, it remains an open question to know when model discovery algorithms based on sparse regression can actually recover the underlying physical processes.
• In this work, we show the design matrices used to infer the equations by sparse regression can violate the irrepresentability condition (IRC) of the Lasso, even when derived from analytical PDE solutions (i.e.
• without additional noise).