arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2311.02004physics.optics
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机器学习方法中的随机激光研究

Investigation of Random Laser in the Machine Learning Approach

Emanuel P. Santos, Rodrigo F. Silva, Célio V. T. Maciel, Daniel F. Luz, Pedro F. A. Silva

AI总结:

本文利用监督学习模型,以自发辐射和泵浦强度为输入预测随机激光强度波动,验证了机器学习再现随机激光随机特性的可行性。

摘要:

机器学习和深度学习是属于人工智能领域的计算工具。近年来,大量研究工作推动了机器学习和深度学习在光学与光子学等多个领域的应用。本文中,我们采用机器学习算法研究预测一种随机现象——随机激光发射——的可行性。结果表明,机器学习和深度学习能够准确再现随机激光所特有的波动。通过使用简单的监督学习算法,我们证明,将自发辐射和泵浦强度作为模型的输入参数,可以预测随机激光的强度波动。文中还讨论了基于所展示结果的应用。

英文摘要:

Machine Learning and Deep Learning are computational tools that fall within the domain of artificial intelligence. In recent years, numerous research works have advanced the application of machine and deep learning in various fields, including optics and photonics. In this article, we employ machine learning algorithms to investigate the feasibility of predicting a stochastic phenomena: random laser emissions. Our results indicate that machine and deep learning have the capacity to accurately reproduce fluctuations characteristic of random lasers. By employing simple supervised learning algorithms, we demonstrate that the random laser intensity fluctuations can be predicted using spontaneous emission and pump intensity as input parameters in the models. Applications based on the demonstrated results are discussed. Keywords: Machine Learning, Deep Learning, Random Laser.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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