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Deep learning for predicting the evolution of chiral active particles motion based on experimental platform
Chiral active particles (CAPs) exhibit intricate collective behaviors, yet their characterization via traditional simulations or experiments is often labor-intensive and resource-demanding. To overcome these challenges, we introduce an integrated experimental platform and deep learning (DL) framewor...
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| Main Authors: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
IOP Publishing
2025-01-01
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| Colecção: | New Journal of Physics |
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1088/1367-2630/adfb27 |
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