Multi-Scale Modeling and Predictive Control of Active Brownian Particles
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908524503629824 |
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| author | Saremi, Sadra Kordbacheh, Amirhossein Ahmadkhan |
| author_facet | Saremi, Sadra Kordbacheh, Amirhossein Ahmadkhan |
| contents | Active Brownian particles (ABPs) function as self-driving agents that display non-equilibrium behavior through their pairwise interactions which lead to phase separation and vortex patterns in both soft matter and living systems. A multiscale approach needs to link particle-level random motion to collective density evolution for proper management of these dynamic systems. Our research delivers a unified control system for ABP groups through particle-based simulation and spectral continuum modeling alongside model predictive control and deep learning forecasting. The N-particle Brownian dynamics simulations implement Weeks-Chandler-Andersen potential to model excluded-volume interactions while incorporating thermal noise and angular velocity modulation with wavelength $λ$. The forced advection-diffusion equation describes the coarse-grained density evolution which the FTCS spectral space solver solves. A new MPC approach uses complex-valued density states to minimize immediate tracking errors against sinusoidal spatial setpoints with actuator limits and control penalties. The hybrid deep neural network combines Conv1D and LSTM and multi-head attention to learn future density profiles from simulated snapshot sequences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06217 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Scale Modeling and Predictive Control of Active Brownian Particles Saremi, Sadra Kordbacheh, Amirhossein Ahmadkhan Soft Condensed Matter Active Brownian particles (ABPs) function as self-driving agents that display non-equilibrium behavior through their pairwise interactions which lead to phase separation and vortex patterns in both soft matter and living systems. A multiscale approach needs to link particle-level random motion to collective density evolution for proper management of these dynamic systems. Our research delivers a unified control system for ABP groups through particle-based simulation and spectral continuum modeling alongside model predictive control and deep learning forecasting. The N-particle Brownian dynamics simulations implement Weeks-Chandler-Andersen potential to model excluded-volume interactions while incorporating thermal noise and angular velocity modulation with wavelength $λ$. The forced advection-diffusion equation describes the coarse-grained density evolution which the FTCS spectral space solver solves. A new MPC approach uses complex-valued density states to minimize immediate tracking errors against sinusoidal spatial setpoints with actuator limits and control penalties. The hybrid deep neural network combines Conv1D and LSTM and multi-head attention to learn future density profiles from simulated snapshot sequences. |
| title | Multi-Scale Modeling and Predictive Control of Active Brownian Particles |
| topic | Soft Condensed Matter |
| url | https://arxiv.org/abs/2509.06217 |