Qing-Tong Dong

An interpretable data-driven identification of dynamical systems via universal neural ordinary differential equations

An interpretable identification framework based on universal neural ordinary differential equations (UNODEs), symbolic regression, and parameter refinement is developed that is competitive on autonomous polynomial systems and more effective in recovering compact symbolic structures for non-polynomial and explicitly time-varying dynamics.