Reduced-Order Inference with Structure-Preserving Parametrization for Bending and Rotating Systems

TL;DR

This paper introduces a structure-preserving reduced-order modeling method for mechanical systems experiencing bending and rotation, enabling fast and accurate simulations from limited data.

Contribution

It proposes a novel operator inference approach with structure-preserving parametrization for second-order systems, specifically targeting bending and rotating mechanical behaviors.

Findings

Effective in capturing system behavior with limited data

Preserves physical properties like symmetry and skew-symmetry

Demonstrated success on three numerical examples

Abstract

Mechanical systems are often characterized only by their response to certain loads known from experiments or simulations. The obtained data can be used for various purposes: system analysis, design of mathematical models, or construction of reduced-order models for further simulations under different loading conditions. The use of data for reduced-order modeling is an important developing research direction, especially when the high-dimensional system operators are unknown and their low-dimensional approximation is required for accurate but fast simulations. Our goal is to obtain the low-dimensional surrogate model from the available input signal and deformation trajectory data, capturing the correct system behavior for the basic deformation cases, namely bending and rotation, which are present in almost every complex mechanical system. In this work, we propose a methodology to infer the system operators by solving a nonlinear unconstrained optimization problem. The methodology is based on the operator inference approach for second-order systems and includes a parametrization of the unknown operators that preserves their symmetric positive definite or skew-symmetric structure. We demonstrate the performance of the novel approach for three numerical examples that are used to simulate basic bending and rotating.