A multi-fidelity poroelastic finite element and machine learning framework for characterizing respiratory mechanics in porcine lungs.

Edwin E Aigbokhan, Olusola A Olabanjo, Emmanuel A Akor, David W Kaczka, Mingchao Cai

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Article in Biomechanics and modeling in mechanobiology, 2026. 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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5 · Who and what money

Authors and funding

5 authors.

Edwin E AigbokhanDepartment of Mathematics, Morgan State University, Baltimore, MD, USA.

Olusola A OlabanjoDepartment of Mathematics, Morgan State University, Baltimore, MD, USA.

Emmanuel A AkorRoy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA.

David W KaczkaRoy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA.

Mingchao CaiDepartment of Mathematics, Morgan State University, Baltimore, MD, USA. mingchao.cai@morgan.edu.

Funding

Iowa Lung Imaging Training ProgramT32HL144461 · NHLBI · UNIVERSITY OF IOWA · PI HOFFMAN, ERIC ALFRED, REINHARDT, JOSEPH M · 2019 to 2023

$1.6M

Army Research Office W911NF-23-1-0004NHLBI NIH HHS T32 HL144461

6 · The paper itself

Abstract

Accurate and rapid characterization of lung mechanics remains a central challenge in respiratory disease management. Physics-informed poroelastic finite-element (FE) models resolve detailed tissue-airflow interactions but are computationally prohibitive for real-time or large-scale clinical applications, while lumped-parameter models sacrifice mechanistic fidelity for efficiency. In this work, we present a porcine-specific, multi-fidelity computational framework that integrates poroelastic FE modeling with machine learning to enable rapid, uncertainty-aware estimation of respiratory compliance (

Indexed as

ElasticityFinite Element AnalysisLungMachine LearningRespiratory MechanicsAnimalsBiomechanical PhenomenaComputer SimulationModels, BiologicalPorosityPressureSwineFinite-element modelingLung biomechanicsMulti-fidelity Gaussian processParameter identificationPoroelasticitySobol sensitivity analysisUncertainty quantification

Identifiers

PMID42384238

DOI10.1007/s10237-026-02100-7

PMCPMC13323121

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LicenceCC BY

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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 Socratic graph.