Learning Hydro-Phoretic Interactions in Active Matter
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915708859842560 |
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| author | Bera, Palash Mukhopadhyay, Aritra K. Liebchen, Benno |
| author_facet | Bera, Palash Mukhopadhyay, Aritra K. Liebchen, Benno |
| contents | In the quest to understand large-scale collective behavior in active matter, the complexity of hydrodynamic and phoretic interactions remains a fundamental challenge. To date, most works either focus on minimal models that do not (fully) account for these interactions, or explore relatively small systems. The present work develops a generic method that combines high-fidelity simulations with symmetry-preserving descriptors and neural networks to predict hydro-phoretic interactions directly from particle coordinates (effective interactions). This method enables, for the first time, self-contained particle-only simulations and theories with full hydro-phoretic pair interactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02277 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Hydro-Phoretic Interactions in Active Matter Bera, Palash Mukhopadhyay, Aritra K. Liebchen, Benno Soft Condensed Matter Disordered Systems and Neural Networks Fluid Dynamics In the quest to understand large-scale collective behavior in active matter, the complexity of hydrodynamic and phoretic interactions remains a fundamental challenge. To date, most works either focus on minimal models that do not (fully) account for these interactions, or explore relatively small systems. The present work develops a generic method that combines high-fidelity simulations with symmetry-preserving descriptors and neural networks to predict hydro-phoretic interactions directly from particle coordinates (effective interactions). This method enables, for the first time, self-contained particle-only simulations and theories with full hydro-phoretic pair interactions. |
| title | Learning Hydro-Phoretic Interactions in Active Matter |
| topic | Soft Condensed Matter Disordered Systems and Neural Networks Fluid Dynamics |
| url | https://arxiv.org/abs/2601.02277 |