Personalizable Models of Cardiac Elasticity with Physics-informed Symbolic Regression

Name: Personalizable Models of Cardiac Elasticity with Physics-informed Symbolic Regression
Uploaded: 2026-10-10T15:06:05.482231+00:00
Duration: 427 s
Description: CHESRA is a physics-informed machine learning framework that automatically derives simple strain energy functions from experimental data to create parsimonious models for cardiac tissue mechanics. It discovers low-complexity SEFs, like $\psi_{CH1}$ and $\psi_{CH2}$, that fit data accurately while using fewer parameters than existing models, promising better personalized cardiac digital twins.

CHESRA is a physics-informed machine learning framework that automatically derives simple strain energy functions from experimental data to create parsimonious models for cardiac tissue mechanics. It discovers low-complexity SEFs, like $\psi_{CH1}$ and $\psi_{CH2}$, that fit data accurately while using fewer parameters than existing models, promising better personalized cardiac digital twins.

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