An integrated data-driven computational pipeline with model order reduction for industrial and applied mathematics

TL;DR

This paper introduces a modular, data-driven computational pipeline that integrates various model order reduction techniques to enhance industrial and applied mathematics applications, facilitating efficient product and process design.

Contribution

It presents a comprehensive, automated framework combining geometric parameterization, dimension reduction, and non-intrusive model order reduction methods for industrial use.

Findings

Effective integration of model order reduction techniques demonstrated on industrial examples

Enhanced efficiency in product and process design workflows

Modular pipeline easily adaptable to existing systems

Abstract

In this work we present an integrated computational pipeline involving several model order reduction techniques for industrial and applied mathematics, as emerging technology for product and/or process design procedures. Its data-driven nature and its modularity allow an easy integration into existing pipelines. We describe a complete optimization framework with automated geometrical parameterization, reduction of the dimension of the parameter space, and non-intrusive model order reduction such as dynamic mode decomposition and proper orthogonal decomposition with interpolation. Moreover several industrial examples are illustrated.