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. To address this, we propose biomimetic physics-informed neural networks (Bio-PINNs) for cell-induced phase transitions in fibrous extracellular matrices. The method converts the outward progression of cell-mediated remodelling into a distance-based training curriculum and couples it to uncertainty-driven collocation that concentrates samples near evolving interfaces and tether-forming regions. The same uncertainty proxy provides a lower-cost alternative to explicit second-derivative regularization. We also establish structural guarantees for the adaptive sampler, including persistent coverage under gate expansion and quantitative near-to-far accumulation. Across single- and multi-cell benchmarks, diverse separations, and various regularization regimes, Bio-PINNs consistently recover sharp transition layers and tether morphologies, significantly outperforming state-of-the-art adaptive and ungated baselines.

翻译：在多孔细胞诱导的相变过程中，非凸多阱能量函数导致微尺度精细结构、低正则性过渡层和尖锐界面的产生，这给物理信息学习带来了数值挑战。为解决这一问题，我们提出了仿生物理信息神经网络（Bio-PINNs），用于纤维细胞外基质中的细胞诱导相变。该方法将细胞介导重塑的外向扩展过程转化为基于距离的训练课程，并将其与不确定性驱动的配置点生成策略耦合，使采样点集中在移动界面和系链形成区域附近。同一不确定性代理为显式二阶导数正则化提供了低成本替代方案。我们还为自适应采样器建立了结构性保证，包括门控扩展下的持续覆盖以及定量近程-远程积累。在单细胞与多细胞基准测试、不同分离距离及多种正则化机制下，Bio-PINNs始终能恢复尖锐过渡层和系链形态，显著优于最先进的自适应和无门控基线模型。