Stochastic modeling of deterministic laser chaos using generator extended dynamic mode decomposition
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915705554731008 |
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| author | Fukushi, Kakutaro Ohkubo, Jun |
| author_facet | Fukushi, Kakutaro Ohkubo, Jun |
| contents | Recently, chaotic phenomena in laser dynamics have attracted much attention to its applied aspects, and a synchronization phenomenon, leader-laggard relationship, in time-delay coupled lasers has been used in reinforcement learning. In the present paper, we discuss the possibility of capturing the essential stochasticity of the leader-laggard relationship; in nonlinear science, it is known that coarse-graining allows one to derive stochastic models from deterministic systems. We derive stochastic models with the aid of the Koopman operator approach, and we clarify that the low-pass filtered data is enough to recover the essential features of the original deterministic chaos, such as peak shifts in the distribution of being the leader and a power-law behavior in the distribution of switching-time intervals. We also confirm that the derived stochastic model works well in reinforcement learning tasks, i.e., multi-armed bandit problems, as with the original laser chaos system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05798 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stochastic modeling of deterministic laser chaos using generator extended dynamic mode decomposition Fukushi, Kakutaro Ohkubo, Jun Applied Physics Recently, chaotic phenomena in laser dynamics have attracted much attention to its applied aspects, and a synchronization phenomenon, leader-laggard relationship, in time-delay coupled lasers has been used in reinforcement learning. In the present paper, we discuss the possibility of capturing the essential stochasticity of the leader-laggard relationship; in nonlinear science, it is known that coarse-graining allows one to derive stochastic models from deterministic systems. We derive stochastic models with the aid of the Koopman operator approach, and we clarify that the low-pass filtered data is enough to recover the essential features of the original deterministic chaos, such as peak shifts in the distribution of being the leader and a power-law behavior in the distribution of switching-time intervals. We also confirm that the derived stochastic model works well in reinforcement learning tasks, i.e., multi-armed bandit problems, as with the original laser chaos system. |
| title | Stochastic modeling of deterministic laser chaos using generator extended dynamic mode decomposition |
| topic | Applied Physics |
| url | https://arxiv.org/abs/2506.05798 |