J. Harlim

A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series

This work empirically verify that the weak formulation, with a proper choice of test function and integration domain, effectively filters noisy data and explains why a weak form loss function is analogous to fitting a model to filtered data and provides a practical way to parameterize the weak form.