用神经网络自动发现心房组织力学模型，突破传统预设模型局限。
Atrial constitutive neural networks
- 基于实验数据自动学习心房组织的本构模型
- 无需人工预设模型，直接从数据中挖掘最优形式
- 适合心脏力学仿真与健康预测研究者使用
本文提出一种新型方法，利用构造性神经网络表征心房组织的力学行为。基于健康人房室组织的实验双轴拉伸测试数据，该方法自动发现最合适的本构材料模型，克服了传统预设模型的局限性。此方法为心房力学建模提供了新视角，是提升心脏模拟与健康预测能力的重要一步。
原文摘要 · Abstract (English)
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.
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