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Overcoming Challenges in Predictive Modeling of Laser-Plasma Interaction Scenarios Chapter 5 Overcoming Challenges in Predictive Modeling of Laser-Plasma Interaction Scenarios. The Sinuous Route from Advanced Machine Learning to Deep Learning Andreea Mihailescu Additional information is available at the end of the chapter http://dx.doi.org/10.5772/intechopen.72844 Abstract The interaction of ultrashort and intense laser pulses with solid targets and dense plasmas is a rapidly developing area of physics, this being mostly due to the significant advance- ments in laser technology. There is, thus, a growing interest in diagnosing as accurately as possible the numerous phenomena related to the absorption and reflection of laser radia- tion. At the same time, envisaged experiments are in high demand of increased accuracy simulation software. As laser-plasma interaction modelings are experiencing a transition from computationally-intensive to data-intensive problems, traditional codes employed so far are starting to show their limitations. It is in this context that predictive modelings of laser-plasma interaction experiments are bound to reshape the definition of simulation software. This chapter focuses an entire class of predictive systems incorporating big data, advanced machine learning algorithms and deep learning, with improved accuracy and speed. Making use of terabytes of already available information (literature as well as simulation and experimental data) these systems enable the discovery and understanding of various physical phenomena occurring during interaction, hence allowing researchers to set up controlled experiments at optimal parameters. A comparative discussion in terms of challenges, advantages, bottlenecks, performances and suitability of laser-plasma inter- action predictive systems is ultimately provided. Keywords: predictive modeling, machine learning, deep learning, big data, cloud computing, laser-plasma interaction modeling 1. Introduction to laser-plasma interaction simulations Numerous significant technological advancements mark the nearly six decades that have elapsed since the invention of the laser. We are nowadays facing a dramatic increase in terms of attainable laser powers and intensities concomitantly with a drastic shortening of pulses © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 84 Machine Learning - Advanced Techniques and Emerging Applications duration. Super-intense lasers such as HERCULES [1], TPL [2], Vulcan [3] and Astra Gemini [4] or PHELIX [5] constitute a notable achievement in terms of chirped pulses intensity: an increase by six orders of magnitude within less than 10 years. Next generation 10-PW laser systems are currently under consideration in various laboratories around the world. To resume to just one example, the 10-PW ILE APOLLON [6] is envisaged to deliver an energy of 150 J in 15 fs at the last stage of amplification after the front end, with a repetition rate of one shot per À minute, its intensity being expected to reach 1024 WÁcm 2. Such elevated intensities are the foregoers of the so called ultrarelativistic regime applications, a regime in which not only the electrons but also the ions become relativistic within one laser period. As matter under extreme conditions can now be relatively easily generated and investigated, we are witnessing a worldwide advent of laboratory research in totally “new physics”, from ultrarelativistic laser plasmas to high-energy particle acceleration and generation of high-frequency radiation in the extreme-ultraviolet (XUV) and soft-X-ray regions. X-ray production by means of high intensity laser-plasma interaction experiments is of particular interest for the scientific community since this is a way of attaining increased brightness X-rays, with good coherence and consequently high quality sources of radiation. Among the variety of laser-based mechanisms deployed for this purpose, the most notable are betatron generation from laser wakefield acceleration [7] and high-order harmonics generation (HHG) [8]. In spite of the multitude of opportunities, there are still technological issues to be addressed and there are still numerous phenomena occurring during the interaction that are not yet fully understood. Some of these may be potentially damaging to experiments (e.g. hydrodynamic or parametric instabilities, hot electrons), hence their mitigation is vital. Ultimately, optimizing interaction conditions requires state-of-the-art theoretical and computational investigations. In terms of simulation software, traditional approaches entail either hydrodynamic (fluid) or kinetic codes, in accordance with the laser-plasma interaction regime. Often, choosing between the two implies an inevitable dismissal of certain phenomena within reasonable accuracy limits. Modeling processes like particles’ acceleration, plasma heating, parametric instabilities that occur during the interaction of ultrashort (pulse duration of sub-picoseconds down to tens À of femtoseconds) and intense (intensity higher than 1017 WÁcm 2) laser pulses with plasma requires mainly a kinetic treatment and this is normally achieved through the Particle-In-Cell method (PIC) [9], the most reputed among the numerical tools employed in plasma physics and in laser-plasma interaction investigations. Albeit being recognized as a suitable approach for analyzing the highly transient physical processes in the non-linear regime associated with ultrafast laser energy coupling to matter, PIC based codes are subject to nonphysical behaviors such as statistical noise, non-physical instabilities, non-conservation, and numerical heating. Secondly, they require considerable computational resources, being far more demanding than the fluid ones that are normally deployed to study phenomena on a nanosecond scale with “coarser” accuracy. For instance, running a 1D PIC with a reasonable number of particles per cell, a fine grid and a small time resolution can claim up to more than 20 CPU hours on a single-processor PC for simulating what happens during a few femtoseconds of interaction. The distribution function at any given time, in a 3D3V PIC code is six dimensional in nature. Should 100 grid points be allocated for each dimension and representing each grid point in eight byte double precision, then, the system would require as far as 7 TB alone, just to store this data structure. In spite of the recent advent of computing technologies, running high Overcoming Challenges in Predictive Modeling of Laser-Plasma Interaction Scenarios. The Sinuous Route from… 85 http://dx.doi.org/10.5772/intechopen.72844 accuracy 3D or even 2D kinetic simulations is still a challenging task even if we are talking about a full migration towards the GPUs. Various simplified codes have been hitherto been built and successfully used with reasonable compromises between accuracy on one hand and storage requirements and speed on the other. The LPIC++ [10, 11], XOOPIC [12] and PIConGPU [13] are some good examples in this sense. Restraining the number of dimensions, in conjunction either with object oriented programming, either with code parallelization, makes it possible to gain increased resolution (but over fewer dimensions) with less fancy hardware. Among the state-of-the-art PICs employed for simulating a variety of laser-plasma problems are the well-established EPOCH [14], VSim [15], OSIRIS [16–18], and QuickPIC [19, 20]. Fully relativistic, parallelized and multidimensional, they all incorporate additional features accounting for phenomena normally disregarded by traditional PIC methods, therefore moving the simulations closer to the real world. For example, EPOCH includes multiphoton, tunneling and collisional ionisations. The latter two can also be found in OSIRIS. VSim is a hybrid code (combining kinetic and hydrodynamic treatments), while OSHUN [21, 22] permits the user to introduce multiple ion species. At the same time, system resources can be spared by either reducing the number of dimensions (user option encountered in EPOCH) or by separating out the time scale of the evolution of the driver from the plasma evolution, thus transforming a fully 3D electromagnetic field solve and particle push into a sequence of 2D solves and pushes (QuickPIC’s algorithm). Highly optimized to run even on a single CPU, these codes are scalable over a large number of cores, featuring the dynamic load balancing of the processors. Parallelization approaches include not only the MPI and Open MP but SIMD Vectorization, with most of these above mentioned simulation environments having CUDA enabled versions as well. Running a PIC code on top of the line GeForce or on Tesla can lead to significant improvements in terms of speed [23–32] while maintaining a fairly large numberofparticlespercell.Breakthroughs have been reported especially with the particle push [33–35] and particle weighing [36–38] algorithms but also with the parallelization during the current deposition phase [39, 40]. Success- ful attempts of integrating these schemes while trying to mitigate some of the factors known to limit GPU performance—communication overhead between GPU and CPU, memory latency versus bandwidth, the relatively low level of multitasking or I/O efficient management when reading and writing to files—count in Jasmine [41, 42] or FBPIC [43, 44]. As cloud, big data and AI based technologies
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