Summary of Veni, Vindy, Vici: a Variational Reduced-order Modeling Framework with Uncertainty Quantification, by Paolo Conti et al.
VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantification
by Paolo Conti, Jonas Kneifl, Andrea Manzoni, Attilio Frangi, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Dynamical Systems (math.DS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research aims to develop a new approach for solving high-dimensional systems of partial differential equations (PDEs), which is essential in various engineering and scientific applications. The authors focus on reducing computational costs by employing reduced-order models (ROMs). However, traditional ROMs often struggle when the governing equations are unknown or partially known, leading to unreliable predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have developed ways to quickly solve complex problems involving partial differential equations (PDEs), which is important in many fields. To make calculations faster, they use reduced-order models (ROMs). But there’s a problem: these ROMs don’t work well when we’re not sure what the underlying rules are or if some of those rules are missing. |
