Atrial constitutive neural networks
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Abstract (arXiv)
This work presents a novel approach for characterizing the mechanical behavior of atrial tissue using constitutive neural networks. Based on experimental biaxial tensile test data of healthy human atria, we automatically discover the most appropriate constitutive material model, thereby overcoming the limitations of traditional, pre-defined models. This approach offers a new perspective on modeling atrial mechanics and is a significant step towards improved simulation and prediction of cardiac health.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 57 references that are in the map. First posted on arXiv 3 Apr 2025.
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References · 9
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2022A new family of Constitutive Artificial Neural Networks towards automated model discoverycited by 49
- 2023Neural networks meet hyperelasticity: A guide to enforcing physicscited by 46
- 2023On sparse regression, Lp-regularization, and automated model discoverycited by 15
- 2022WarpPINN: Cine-MR image registration with physics-informed neural networkscited by 14
- 2024A universal material model subroutine for soft matter systemscited by 4
- 2024Assessment of Left Atrium Motion Deformation Through Full Cardiac Cyclecited by 2
- 2024Ensemble learning of the atrial fiber orientation with physics-informed neural networkscited by 1
- 2025Discovering uncertainty: Gaussian constitutive neural networks with correlated weightscited by 1
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