Physics-Guided Surrogate Modeling for Machine Learning-Driven DLD Design Optimization
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912751462383616 |
|---|---|
| author | Islam, Khayrul Hasan, Mehedi Liu, Yaling |
| author_facet | Islam, Khayrul Hasan, Mehedi Liu, Yaling |
| contents | Sorting cells based on their mechanical properties is essential for applications in disease diagnostics, cell therapy, and biomedical research. Deterministic Lateral Displacement (DLD) devices provide a label-free method for achieving such sorting, but their performance is highly sensitive to cell size and deformability. Designing effective DLD geometries often demands extensive trial-and-error experimentation, as even small variations in cellular mechanical traits can cause significant changes in migration behavior. To address this challenge, we propose a simulation-driven machine learning (ML) framework that predicts suitable DLD design candidates for a given cell type. Our approach integrates high-fidelity particle-based simulations to model cell deformation and migration through microfluidic pillar arrays with supervised ML models trained to estimate optimal geometries. By mapping mechanical parameters such as bending rigidity and shear modulus to deformation index and migration angle, the framework enables rapid, data-informed design of DLD systems. We also demonstrate a deployable web interface to make this tool accessible for real-world device prototyping. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_05649 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-Guided Surrogate Modeling for Machine Learning-Driven DLD Design Optimization Islam, Khayrul Hasan, Mehedi Liu, Yaling Quantitative Methods Sorting cells based on their mechanical properties is essential for applications in disease diagnostics, cell therapy, and biomedical research. Deterministic Lateral Displacement (DLD) devices provide a label-free method for achieving such sorting, but their performance is highly sensitive to cell size and deformability. Designing effective DLD geometries often demands extensive trial-and-error experimentation, as even small variations in cellular mechanical traits can cause significant changes in migration behavior. To address this challenge, we propose a simulation-driven machine learning (ML) framework that predicts suitable DLD design candidates for a given cell type. Our approach integrates high-fidelity particle-based simulations to model cell deformation and migration through microfluidic pillar arrays with supervised ML models trained to estimate optimal geometries. By mapping mechanical parameters such as bending rigidity and shear modulus to deformation index and migration angle, the framework enables rapid, data-informed design of DLD systems. We also demonstrate a deployable web interface to make this tool accessible for real-world device prototyping. |
| title | Physics-Guided Surrogate Modeling for Machine Learning-Driven DLD Design Optimization |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2512.05649 |