Abstract
<h2>Abstract</h2> Modern reduced order models (ROMs) have widespread applicability in computational science and engineering as they allow accurate simulation of complex, nonlinear problems with minimal computational cost. In this paper, we introduce a Python-based implementation of a suite of data-driven ROM techniques for dynamical systems governed by time-dependent, nonlinear partial differential equations (PDEs). The versatility and accuracy of the presented ROM frameworks have been demonstrated with various numerical experiments in multiple publications. Therefore, this module is suitable not only as a tool for users in the industry, but it also provides a framework for researchers in academia to pursue further development.
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