Physics-Informed Symbolic Regression for Elasticity Modeling in Cardiac Digital Twins

TL;DR

This paper presents CHESRA, a physics-informed symbolic regression method that derives simple, accurate strain energy functions for cardiac tissue modeling, improving personalization and clinical applicability.

Contribution

Introduction of CHESRA, a novel physics-informed symbolic regression framework that automatically discovers minimal-parameter models for cardiac tissue mechanics.

Findings

CHESRA identified two new functions with 3 and 4 parameters.

The functions achieved high data fitting accuracy in experiments.

CHESRA provided more consistent parameter estimates than existing methods.

Abstract

Cardiac digital twins hold great promise for personalized medicine, but they currently depend on complex constitutive models of tissue mechanics that are often over-parameterized for the clinical context. To address this, we introduce CHESRA (Cardiac Hyperelastic Evolutionary Symbolic Regression Algorithm), a physics-informed machine learning framework that automatically derives simple strain energy functions from multiple experimental data sources. Using a normalizing loss function, CHESRA identified two new functions with only three and four parameters, respectively. These functions achieve high data fitting accuracy in experimental scenarios while enabling more consistent parameter estimation than state-of-the-art approaches, both in tissue benchmarks and 3D simulations. By combining biophysical constraints with data-driven discovery, CHESRA demonstrates how physics-informed learning can generate accurate, personalizable models for advancing cardiac digital twins and clinical decision-making.