Evidence map›Paper›PMID 37066991›Full record

ArticleThe Journal of experimental biology2023

Reconstructing the pressure field around swimming fish using a physics-informed neural network.

Michael A Calicchia, Rajat Mittal, Jung-Hee Seo, Rui Ni

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Journal of experimental biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. From PINNs to PIKANs: recent advances in physics-informed machine learning.Machine learning for computational science and engineering · 2025
    Article
  4. Review
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

4 authors.

Michael A CalicchiaThe Department of Mechanical Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
Rajat MittalThe Department of Mechanical Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
Jung-Hee SeoThe Department of Mechanical Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
Rui NiThe Department of Mechanical Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0002-7178-1479

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fish detect predators, flow conditions, environments and each other through pressure signals. Lateral line ablation is often performed to understand the role of pressure sensing. In the present study, we propose a non-invasive method for reconstructing the instantaneous pressure field sensed by a fish's lateral line system from two-dimensional particle image velocimetry (PIV) measurements. The method uses a physics-informed neural network (PINN) to predict an optimized solution for the pressure field near and on the fish's body that satisfies both the Navier-Stokes equations and the constraints put forward by the PIV measurements. The method was validated using a direct numerical simulation of a swimming mackerel, Scomber scombrus, and was applied to experimental data of a turning zebrafish, Danio rerio. The results demonstrate that this method is relatively insensitive to the spatio-temporal resolution of the PIV measurements and accurately reconstructs the pressure on the fish's body.

Indexed as

SwimmingZebrafishAnimalsModels, BiologicalNeural Networks, ComputerPhysicsBiolocomotionParticle image velocimetryPhysics-informed learningPressure reconstructionPressure sensing

Identifiers

PMID37066991
PMCPMC10163353

What OpenQuestion holds

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Registered trials

None linked

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.