以数据为导向,对带有二次波的群体探测器进行数据驱动的探索.
R Senthamizhan1, R Gopal1, V K Chandrasekar1
1Department of Physics, Centre for Nonlinear Science and Engineering, School of Electrical and Electronics Engineering, <a href="https://ror.org/032jk8892">SASTRA Deemed University</a>, Thanjavur 613 401, India.
Physical review. E
|July 18, 2024
概括
我们研究了带有二次波的swarmalators,发现了活跃的异步状态和相波. 卷积神经网络有效地分析了这些复杂的动态和稳定性边界.
科学领域:
- 复杂的系统复杂的系统.
- 非线性动力学是一种非线性动力学.
- 统计物理 统计物理
背景情况:
- 游泳者结合了游泳和振荡的特征.
- 第二阶波引入了相动态中的复杂相互作用.
- 了解合振荡器种群中出现的行为至关重要.
研究的目的:
- 为了研究二阶相相互作用的群体分子的动态状态.
- 识别和描述活跃的异步状态和相波过渡.
- 使用新的方法量化活跃异步状态的稳定边界.
主要方法:
- 模拟二次波相互作用的群体群体.
- 卷积神经网络 (U-Net架构) 的应用,用于数据驱动的稳定性分析.
- 对集群国家的稳定性进行分析研究.
主要成果:
- 观察到活跃的异步状态和集群状态的出现.
- 通过积极的异步状态从静态异步到活性相波状态的过渡被确定.
- 积极异步状态的稳定性边界通过数据驱动方法成功地划定和量化.
结论:
- 群交者中的二次波会导致丰富的动态行为,包括活跃的异步和集群状态.
- 卷积神经网络为分析复杂动态系统的稳定性提供了有效的工具.
- 这项研究表明,数据驱动和分析方法在理解新兴现象方面具有强大的协同作用.
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