An integrated data-driven computational pipeline with model order reduction for industrial and applied mathematics
For engineers and researchers in industrial mathematics, this work provides a modular, data-driven pipeline that integrates existing model order reduction methods, but it is an incremental combination of known techniques rather than a fundamental advance.
The paper presents an integrated computational pipeline that combines multiple model order reduction techniques for industrial and applied mathematics, enabling automated optimization with data-driven and modular components. The pipeline is demonstrated on several industrial examples.
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.