Neural-Network-Assisted Boltzmann Approach for Dilute Microswimmer Suspensions
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909872728047616 |
|---|---|
| author | Hayano, Haruki Furukawa, Akira Kim, Kang |
| author_facet | Hayano, Haruki Furukawa, Akira Kim, Kang |
| contents | We introduce a neural-network-assisted Boltzmann framework that learns the binary-collision map of microswimmers directly from data and uses it to evaluate collision integrals efficiently. Using a representative model swimmer, the learned map quantitatively predicts translational and rotational diffusivities and enables a linear-stability analysis of isotropy against polar ordering in dilute suspensions. The resulting predictions closely match direct simulations. The present framework is agnostic to active matter models and broadly applicable: once two-body collision data are obtained -- either from simulations or experiments -- the same surrogate can be used to evaluate kinetic transport across dilute conditions where binary collisions dominate. Because the workflow relies only on pre- and post-collision statistics, the present approach provides a general data-driven route linking particle-scale interactions to macroscopic transport and collective behavior in active suspensions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02559 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural-Network-Assisted Boltzmann Approach for Dilute Microswimmer Suspensions Hayano, Haruki Furukawa, Akira Kim, Kang Soft Condensed Matter We introduce a neural-network-assisted Boltzmann framework that learns the binary-collision map of microswimmers directly from data and uses it to evaluate collision integrals efficiently. Using a representative model swimmer, the learned map quantitatively predicts translational and rotational diffusivities and enables a linear-stability analysis of isotropy against polar ordering in dilute suspensions. The resulting predictions closely match direct simulations. The present framework is agnostic to active matter models and broadly applicable: once two-body collision data are obtained -- either from simulations or experiments -- the same surrogate can be used to evaluate kinetic transport across dilute conditions where binary collisions dominate. Because the workflow relies only on pre- and post-collision statistics, the present approach provides a general data-driven route linking particle-scale interactions to macroscopic transport and collective behavior in active suspensions. |
| title | Neural-Network-Assisted Boltzmann Approach for Dilute Microswimmer Suspensions |
| topic | Soft Condensed Matter |
| url | https://arxiv.org/abs/2510.02559 |