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arXiv · 2608.00082

Data-Driven Model Order Reduction with pyMOR

Hendrik Kleikamp·Petar Mlinarić·Stephan Rave·Felix Schindler
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Abstract

pyMOR is a free and open-source software library of model order reduction algorithms for the Python programming language. Designed with classical model-based reduction methods for large-scale parametric partial differential equation problems in mind, algorithms in pyMOR are implemented in terms of operations on abstract VectorArray, Operator and Model interfaces, allowing for a seamless integration with external solver codes implementing the full-order model. For cases where a tight integration with the full-order model code is not feasible, data-driven model order reduction algorithms, which only require simulation or measurement data of the full-order model, are an attractive alternative. In this work we discuss the data-driven methods that have been recently added to pyMOR, show practical examples of their application using pyMOR and compare their performance with classical model-based methods. We show that pyMOR serves as a unified framework for combining model-based and data-driven methods, enabling the construction of flexible and efficient hierarchical model reduction pipelines.

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Hendrik Kleikamp, Petar Mlinarić, Stephan Rave, Felix Schindler. 2026-07-29. Data-Driven Model Order Reduction with pyMOR. https://arxiv.org/abs/2608.00082

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