Nonintrusive Learning of Nonlinear Lagrangian Reduced-Order Models for Mechanical Systems

A two-step approach is proposed to learn nonlinear Lagrangian reduced-order models (ROMs) of nonlinear mechanical systems directly from data, without requiring access to the full-order model operators. The method first learns a linear Lagrangian ROM via Lagrangian operator inference and then augments it with nonlinear terms learned using structure-preserving machine learning.