他们用物理模型加Transformer，能从肌肉收缩曲线提取参数，还测了杜氏肌营养不良样本，搞组织工程的可看看。
该研究提出物理风格神经网络（PFNN），将拉伸指数物理模型集成到CNN-Transformer中，直接从力-时间曲线提取物理参数。为解决标记数据稀缺，模型先在合成数据上建立物理直觉，再通过无监督自对齐适配未标记的真实测量数据。在多种收缩表型和细胞系上实现高保真参数化，包括Duchenne肌营养不良模型。该管道可扩展并自我改进，适用于高通量生物物理研究。
A Physics-Flavored Transformer Network for Parametrizing Contraction Dynamics of Engineered Skeletal Muscle Tissues
Engineered Skeletal Muscle Tissues (ESMs) have become a key structure for biomedical disease modeling and pharmacological screening, yet their functional characterization often relies on simplistic metrics like peak force, discarding critical kinetic information. This is partially due to the high level of mathematical complexity which mechanistic models introduce to capture these dynamics. Hence, exactly the complexity prevents scalable application and widespread adaptation in the field. Here we present a Physics-Flavored Neural Network (PFNN) that automates the kinetic phenotyping of ESMs. Our architecture integrates a stretched-exponential physical model into a CNN-Transformer, enabling the extraction of physically meaningful parameters directly from force-time profiles. To address the scarcity of labeled biological data, we employ a hybrid training paradigm: the model develops a "physical intuition" on synthetic data before undergoing unsupervised self-alignment on unlabeled real-world measurements. Our results demonstrate that this physics-flavored approach achieves high-fidelity parameterization across diverse contractile phenotypes and cell lines, including Duchenne Muscular Dystrophy models. Our scalable, self-improving pipeline bridges the gap between idealized biophysics and noisy \emph{in vitro} data, providing a robust tool for high-throughput biophysical research.