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arXiv · 2003.07798

Pressio: Enabling projection-based model reduction for large-scale nonlinear dynamical systems

Francesco Rizzi·Patrick J. Blonigan·Eric J. Parish·Kevin T. Carlberg
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Abstract

This work introduces Pressio, an open-source project aimed at enabling leading-edge projection-based reduced order models (ROMs) for large-scale nonlinear dynamical systems in science and engineering. Pressio provides model-reduction methods that can reduce both the number of spatial and temporal degrees of freedom for any dynamical system expressible as a system of parameterized ordinary differential equations (ODEs). We leverage this simple, expressive mathematical framework as a pivotal design choice to enable a minimal application programming interface (API) that is natural to dynamical systems. The core component of Pressio is a C++11 header-only library that leverages generic programming to support applications with arbitrary data types and arbitrarily complex programming models. This is complemented with Python bindings to expose these C++ functionalities to Python users with negligible overhead and no user-required binding code. We discuss the distinguishing characteristics of Pressio relative to existing model-reduction libraries, outline its key design features, describe how the user interacts with it, and present two test cases -- including one with over 20 million degrees of freedom -- that highlight the performance results of Pressio and illustrate the breath of problems that can be addressed with it.

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Francesco Rizzi, Patrick J. Blonigan, Eric J. Parish, Kevin T. Carlberg. 2021-09-01. Pressio: Enabling projection-based model reduction for large-scale nonlinear dynamical systems. https://arxiv.org/abs/2003.07798

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