Pressio is an open-source computational framework aimed at advancing the field of reduced-order models (ROMs) for dynamical systems in science and engineering.
Model reduction is a broad and very active field. Many methods exist, but there is no such thing as "one method to rule them all". We believe that evaluating the quality of a reduced model requires accounting for several factors, e.g., the reduction in degrees of freedom, training cost, evaluation cost, robustness, simplicity, predictive accuracy, etc. There is no single metric to rely on; it is always a tradeoff.
We believe that there is a lot to explore in this field both in terms of new research directions as well as assessing robustness of current state-of-the-art methods. There is no better way than an agile Python framework to incentivize and foster work to impact this field. Working towards this goal, pressio4py is our open source contribution to research novel fundamental ideas on model reduction as well as test state-of-the-art methods on problems of arbitrary complexity and from arbitrary disciplines. Python is the perfect language to do so because it benefits from a large community of developers and has become the de-facto choice for machine learning. This offers an ideal framework to explore and merge ideas from different fields.
Open an issue on github, or find us on Slack: https://pressioteam.slack.com.
The full license is available here.
We are working on publishing this: you can find our arXiv preprint at: https://arxiv.org/abs/2003.07798.