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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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GrooveSquid.com Paper Summaries

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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.

Keywords

» Artificial intelligence