Functional role of synchronization: A mean-field control perspective
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912215066476544 |
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| author | Mehta, Prashant Meyn, Sean |
| author_facet | Mehta, Prashant Meyn, Sean |
| contents | The broad goal of the research surveyed in this article is to develop methods for understanding the aggregate behavior of interconnected dynamical systems, as found in mathematical physics, neuroscience, economics, power systems and neural networks. Questions concern prediction of emergent (often unanticipated) phenomena, methods to formulate distributed control schemes to influence this behavior, and these topics prompt many other questions in the domain of learning. The area of mean field games, pioneered by Peter Caines, are well suited to addressing these topics. The approach is surveyed in the present paper within the context of controlled coupled oscillators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00590 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Functional role of synchronization: A mean-field control perspective Mehta, Prashant Meyn, Sean Optimization and Control Machine Learning 49N80, 68T05 The broad goal of the research surveyed in this article is to develop methods for understanding the aggregate behavior of interconnected dynamical systems, as found in mathematical physics, neuroscience, economics, power systems and neural networks. Questions concern prediction of emergent (often unanticipated) phenomena, methods to formulate distributed control schemes to influence this behavior, and these topics prompt many other questions in the domain of learning. The area of mean field games, pioneered by Peter Caines, are well suited to addressing these topics. The approach is surveyed in the present paper within the context of controlled coupled oscillators. |
| title | Functional role of synchronization: A mean-field control perspective |
| topic | Optimization and Control Machine Learning 49N80, 68T05 |
| url | https://arxiv.org/abs/2502.00590 |