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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.29184 |
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| _version_ | 1866918478927101952 |
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| author | Lin, Anci Zhang, Zhiwen Zhao, Wenju |
| author_facet | Lin, Anci Zhang, Zhiwen Zhao, Wenju |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29184 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Cell-induced densification and tether formation in fibrous extracellular matrices with biomimetic physics-informed neural networks Lin, Anci Zhang, Zhiwen Zhao, Wenju Machine Learning Numerical Analysis 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. |
| title | Cell-induced densification and tether formation in fibrous extracellular matrices with biomimetic physics-informed neural networks |
| topic | Machine Learning Numerical Analysis |
| url | https://arxiv.org/abs/2603.29184 |