Data-Driven Modeling of a Controlled Orthotropic Plate Using Machine Learning

TL;DR

This paper explores machine learning models to accurately predict the behavior of a controlled orthotropic vibrating plate, addressing challenges posed by material complexity and unknown damping, and finds multilayer perceptrons most effective.

Contribution

It introduces data-driven surrogate models for complex orthotropic plates, comparing different machine learning approaches to identify the most suitable for capturing nonlinear dynamics.

Findings

MLP provides the best approximation among tested models

Surrogate models effectively capture nonlinear behavior

Data-driven approach addresses modeling challenges of orthotropic materials

Abstract

We study the problem of learning the input-output map of a controlled vibrating plate with a composite structure from experimental measurements. Analytical modeling of this control system faces challenges due to the essential orthotropy and unknown damping characteristics of the material. Surrogate models based on linear regression, multilayer perceptrons, and gated recurrent units are constructed from the available sampled data. Through comparative analysis, we show that the multilayer perceptron model provides an acceptable approximation of this dynamical system, capturing the potentially nonlinear phenomena in its input-output behavior.