2022/03/12 by Harsh Sharma, Sharma, Harsh, Boris Kramer +1 · 5 citations
Computer Science · Engineering · Physics and Astronomy · #FOS: Mathematics #Model Reduction and Neural Networks #Modeling and Simulation Systems #Numerical Analysis (math.NA) #Real-time simulation and control systems
paper · pdf · doi:10.48550/arxiv.2203.06361
openalex publication_date 2022/03/12 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28
This work presents a nonintrusive physics-preserving method to learn reduced-order models (ROMs) of Lagrangian systems, which includes nonlinear wave equations. Existing intrusive projection-based model reduction approaches construct structure-preserving Lagrangian ROMs by projecting the Euler-Lagrange equations of the full-order model (FOM) onto a linear subspace. This Galerkin projection step requires complete knowledge about the Lagrangian operators in the FOM and full access to manipulate the computer code. In contrast, the proposed Lagrangian operator inference approach embeds the mechanics into the operator inference framework to develop a data-driven model reduction method that preserves the underlying Lagrangian structure. The proposed approach exploits knowledge of the governing equations (but not their discretization) to define the form and parametrization of a Lagrangian ROM which can then be learned from projected snapshot data. The method does not require access to FOM operators or computer code. The numerical results demonstrate Lagrangian operator inference on an Euler-Bernoulli beam model, the sine-Gordon (nonlinear) wave equation, and a large-scale discretization of a soft robot fishtail with 779,232 degrees of freedom. The learned Lagrangian ROMs generalize well, as they can accurately predict the physical solutions both far outside the training time interval, as well as for unseen initial conditions.