Stepping up theoretical investigations of ultrashort and intense laser pulses with overdense plasmas. Combining particle-in-cell simulations with machine learning and big data

A. Mihăilescu
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引用次数: 1

Abstract

Over the past decade, advances in the laser technology brought about an increase in the maximum achievable laser intensity of six orders. At the same time, the pulse duration was considerably shortened. The interaction of such ultrashort and intense laser pulses with solid targets and dense plasmas is a rapidly developing area of physics. Hence, a growing interest in characterizing as accurately as possible the phenomena of absorption and reflection that occur during this interaction. Particle-in-Cell (PIC) simulations have traditionally been known to be one of the most important numerical tools employed in plasma physics and in laser-plasma interaction investigations. However, PIC codes are subject to non-physical behaviours such as statistical noise, non-physical instabilities, non-conservation, and numerical heating. Secondly, they require considerable computational resources. This paper proposes a novel approach by combining PIC simulations with machine learning in order to derive optimal laser-plasma interaction scenarios for particular given laboratory experiments. Over 2TB of interaction data consisting of PIC output and also of available literature data have been processed using Hadoop and Apache Mahout, respectively. The combination is a reliable tool for estimations of electron temperatures, plasma densities, parametric instabilities, offering valuable insights on potential interaction phenomena.
加强超密等离子体超短强激光脉冲的理论研究。结合细胞内粒子模拟与机器学习和大数据
在过去的十年中,激光技术的进步使最大可实现的激光强度增加了六个数量级。同时,脉冲持续时间大大缩短。这种超短强激光脉冲与固体目标和致密等离子体的相互作用是物理学中一个迅速发展的领域。因此,人们对尽可能准确地描述在这种相互作用中发生的吸收和反射现象越来越感兴趣。粒子池(PIC)模拟一直被认为是等离子体物理和激光等离子体相互作用研究中最重要的数值工具之一。然而,PIC代码受到非物理行为的影响,如统计噪声、非物理不稳定性、非守恒性和数值加热。其次,它们需要大量的计算资源。本文提出了一种将PIC模拟与机器学习相结合的新方法,以获得特定给定实验室实验的最佳激光等离子体相互作用场景。使用Hadoop和Apache Mahout分别处理了由PIC输出和可用文献数据组成的超过2TB的交互数据。该组合是估计电子温度、等离子体密度、参数不稳定性的可靠工具,为潜在的相互作用现象提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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