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Bayesian inference method enables single-shot characterization of cardiac tissue

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an unsupervised method called EUCLID to infer complex material properties of cardiac muscle tissue. This approach utilizes Bayesian inference to analyze data from a single biaxial test, significantly reducing the need for multiple specimens and extensive handling. The method accurately recovers material parameters with quantified uncertainty, even with noisy measurements, offering a more efficient and reliable way to characterize nonlinear orthotropic materials. AI

IMPACT Introduces a novel unsupervised learning method for material characterization, potentially improving efficiency in biological and material science research.

RANK_REASON This is a research paper detailing a new unsupervised method for material property inference. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Bayesian inference method enables single-shot characterization of cardiac tissue

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This is a research paper detailing a new unsupervised method for material property inference. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Rogier P. Krijnen, Akshay Joshi, Siddhant Kumar, Mathias Peirlinck ·

    Unsupervised full-field Bayesian inference of orthotropic hyperelasticity from a single biaxial test: a myocardial case study

    arXiv:2510.09498v3 Announce Type: replace-cross Abstract: Cardiac muscle tissue exhibits highly non-linear hyperelastic and orthotropic material behavior during passive deformation. Traditional constitutive identification protocols therefore combine multiple loading modes and typ…

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