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arXiv · 1810.12364

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

Marco Tezzele·Nicola Demo·Andrea Mola·Gianluigi Rozza
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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.

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Marco Tezzele, Nicola Demo, Andrea Mola, Gianluigi Rozza. 2018-10-29. An integrated data-driven computational pipeline with model order reduction for industrial and applied mathematics. https://doi.org/10.1007/978-3-030-96173-2_7

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