Evidence map›Paper›PMID 42568630›Full record

ArticleAdvanced modeling and simulation in engineering sciences2026

Two-stage ROM: a hybrid reduced order model for offline and online efficiency.

Rahul Dhopeshwar, Harshit Bansal, Karen Veroy

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced modeling and simulation in engineering sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Rahul DhopeshwarCentre for Analysis, Scientific Computing and Applications, Eindhoven University of Technology, Eindhoven, 5600MB The Netherlands.
Harshit BansalCentre for Analysis, Scientific Computing and Applications, Eindhoven University of Technology, Eindhoven, 5600MB The Netherlands.
Karen VeroyCentre for Analysis, Scientific Computing and Applications, Eindhoven University of Technology, Eindhoven, 5600MB The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Physics-based reduced-order models are one of the key enablers for improving the predictive capabilities of digital twins, offering a balance between computational efficiency and physical fidelity. However, their efficiency for nonlinear problems remains a challenge because evaluating nonlinear operators in reduced dimensions can be very costly, and offline construction often requires many high-fidelity simulations. In this paper, we address this issue with a reduced-order modeling strategy for parametrized nonlinear partial differential equations that is efficient both online

Indexed as

Gaussian process regressionGreedy reduced basis methodHyper-reductionNon-linear model order reductionPhysics-based reduced-order models

Identifiers

PMID42568630
PMCPMC13447419

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.