用仿生神经网络模拟细胞诱导的纤维基质致密化与绳状结构形成。
Cell-induced densification and tether formation in fibrous extracellular matrices with biomimetic physics-informed neural networks
- 通过渐进式远近教学策略和形变不确定性代理，聚焦过渡层与绳状区域。
- 在单细胞与多细胞模型中，边界及细胞间隙的致密化恢复更准确。
- 相比传统方法，能更真实捕捉绳状结构形态，适合生物力学模拟研究者。
细胞诱导的相变中存在非凸多阱能量，导致细尺度微观结构、低正则性过渡层与尖锐界面，给物理信息学习带来数值挑战。本文提出仿生物理信息神经网络（Bio-PINNs），采用从近到远的渐进教学策略，逐步揭示远离细胞边界的计算域，并结合形变不确定性代理，将采样点集中于演化的过渡层与绳状结构形成区。在单细胞与多细胞基准测试中，Bio-PINNs 在细胞边界及细胞间缝隙处更可靠地恢复致密相，且对绳状结构形态的捕捉优于代表性无门控与残差驱动的自适应基线方法。
原文摘要 · Abstract (English)
Nonconvex multi-well energies in cell-induced phase transitions give rise to fine-scale microstructures, low-regularity transition layers and sharp interfaces, all of which pose numerical challenges for physics-informed learning. Here we introduce biomimetic physics-informed neural networks (Bio-PINNs), which implement a near-to-far curriculum by progressively revealing the computational domain away from the cell boundary and combining this schedule with a deformation-uncertainty proxy that concentrates collocation points near evolving transition layers and tether-forming regions. Across single-cell and multicellular benchmarks, Bio-PINNs recover the densified phase more reliably near cell boundaries and in intercellular gaps, while capturing tether morphology more faithfully than representative ungated and residual-driven adaptive baselines.
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