Nonlinear parametric models of viscoelastic fluid flows.

C M Oishi, A A Kaptanoglu, J Nathan Kutz, S L Brunton

Abstract read

In one paragraph

Article in Royal Society open science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

C M OishiDepartamento de Matemática e Computação, Faculdade de Ciências e Tecnologia, São Paulo State University, Presidente Prudente, Brazil.ORCID https://orcid.org/0000-0002-0904-6561

A A KaptanogluCourant Institute of Mathematical Sciences, New York University, New York, NY, USA.

J Nathan KutzDepartment of Applied Mathematics, University of Washington, Seattle, WA, USA.ORCID https://orcid.org/0000-0002-6004-2275

S L BruntonDepartment of Mechanical Engineering, University of Washington, Seattle, WA, USA.ORCID https://orcid.org/0000-0002-6565-5118

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reduced-order models (ROMs) have been widely adopted in fluid mechanics, particularly in the context of Newtonian fluid flows. These models offer the ability to predict complex dynamics, such as instabilities and oscillations, at a considerably reduced computational cost. In contrast, the reduced-order modelling of non-Newtonian viscoelastic fluid flows remains relatively unexplored. This work leverages the

Indexed as

computational fluid dynamicsdata-driven modelsmachine learningreduced-order modelssparse identification of nonlinear dynamicsviscoelastic fluids

Identifiers

PMID39469133

DOI10.1098/rsos.240995

PMCPMC11515135

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.