Extreme Events Prediction from Nonlocal Partial Information in a Spatiotemporally Chaotic Microcavity Laser,Physical Review Letters

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Extreme Events Prediction from Nonlocal Partial Information in a Spatiotemporally Chaotic Microcavity Laser
Physical Review Letters ( IF 8.1 ) Pub Date : 2023-05-31 , DOI: 10.1103/physrevlett.130.223801
V A Pammi ₁ , M G Clerc ₂ , S Coulibaly ₃ , S Barbay ₁

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The forecasting of high-dimensional, spatiotemporal nonlinear systems has made tremendous progress with the advent of model-free machine learning techniques. However, in real systems it is not always possible to have all the information needed; only partial information is available for learning and forecasting. This can be due to insufficient temporal or spatial samplings, to inaccessible variables, or to noisy training data. Here, we show that it is nevertheless possible to forecast extreme event occurrences in incomplete experimental recordings from a spatiotemporally chaotic microcavity laser using reservoir computing. Selecting regions of maximum transfer entropy, we show that it is possible to get higher forecasting accuracy using nonlocal data vs local data, thus allowing greater warning times of at least twice the time horizon predicted from the nonlinear local Lyapunov exponent.

中文翻译：

时空混沌微腔激光器中非局域部分信息的极端事件预测

随着无模型机器学习技术的出现，高维时空非线性系统的预测取得了巨大进步。然而，在实际系统中，并非总能获得所需的所有信息；只有部分信息可用于学习和预测。这可能是由于时间或空间采样不足、不可访问的变量或嘈杂的训练数据造成的。在这里，我们表明，使用储层计算从时空混沌微腔激光器中预测不完整实验记录中的极端事件的发生仍然是可能的。选择最大传输熵的区域，我们表明与本地数据相比，使用非本地数据可以获得更高的预测精度，

更新日期：2023-06-01

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