[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126577-en":3,"doc-seo-126577-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126577,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Data-driven science and machine learning methods in laser–plasma physics","Laser-plasma physics has advanced rapidly as powerful lasers have become widely available, shifting research from single-shot studies with limited parameter scans toward experiments and simulations that generate data across hundreds or thousands of settings. Growing big-data capability has increased the use of mathematics, statistics, and computer science techniques to extract value from large datasets, while advanced modeling supports situations where measurements remain sparse. This paper provides an overview of machine learning methods focused on applicability to laser–plasma physics and key subfields like laser-plasma acceleration and inertial confinement fusion.","High Power Laser Science and Engineering, (2023), Vol. 11, e55, 41 pages. doi:10.1017/hpl.2023.47  \nREVIEW  \nData-driven science and machine learning methods in laser–plasma physics  \nAndreas Döpp1,2 , Christoph Eberle1 , Sunny Howard1,2 , Faran Irshad 1 , Jinpu Lin1 , and Matthew Streeter3  \n1Ludwig-Maximilians-Universität München, Garching, Germany  \n2 Department of Physics, Clarendon Laboratory, University of Oxford, Oxford, UK  \n3 School for Mathematics and Physics, Queen’s University Belfast, Belfast, UK (Received 30 November 2022; revised 30 March 2023; accepted 24 May 2023)  \nAbstract  \nLaser-plasma physics has developed rapidly over the past few decades as lasers have become both more powerful and more widely available. Early experimental and numerical research in this field was dominated by single-shot experiments with limited parameter exploration. However, recent technological improvements make it possible to gather data for hundreds or thousands of different settings in both experiments and simulations. This has sparked interest in using advanced techniques from mathematics, statistics and computer science to deal with, and benefit from, big data. At the same time, sophisticated modeling techniques also provide new ways for researchers to deal effectively with situation where still only sparse data are available. This paper aims to present an overview of relevant machine learning methods with focus on applicability to laser-plasma physics and its important sub-fields of laser-plasma acceleration and inertial confinement fusion.  \nKeywords: deep learning; laser–plasma interaction; machine learning  \n1. Introduction  \n1.1. Laser–plasma physics  \nOver the past decades, the development of increasingly powerful laser systems[1,2] has enabled the study of light–matter interaction across many regimes. Of particular interest is the interaction of intense laser pulses with plasma, which is characterized by strong nonlinearities that occur across many scales in space and time[3,4] . These laser–plasma interactions are of interest both for fundamental physics research and as emerging technologies for potentially disruptive applications.  \nRegarding fundamental research, high-power lasers have, for instance, been used to study transitions from classical electrodynamics to quantum electrodynamics (QED) via the radiation reaction, where a particle’s backreaction to its radiation field manifests itself in an additional force[5–7] . Recent proposals to extend intensities to the Schwinger  \nCorrespondence to: Andreas Döpp, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching, Germany. Email:  \n[a.doepp@lmu.de](a.doepp@lmu.de)  \nlimit[8], where the electric field strength of the light is comparable to the Coulomb field, could allow the study of novel phenomena expected to occur due to a breakdown of perturbation theory. In an only slightly less extreme case, high-energy density physics (HEDP)[9] research uses lasers for the production and study of states of matter that cannot be reached otherwise in terrestrial laboratories. This includes creating and investigating material under extreme pressuresand temperatures, leading to exotic states such as warm– dense matter[10–12] .  \nApart from the fundamental interest, there is also considerable interest in developing novel applications that are enabled by these laser–plasma interactions. Two particularly promising application areas have emerged over the past decades, namely the production of high-energy radiation beams (electrons, positrons, ions, X-rays, gamma-rays) and laser-driven fusion.  \nLaser–plasma acceleration (LPA) aims to accelerate charged particles to high energies over short distances by inducing charge separation in the plasma, for example, in the form of plasma waves to accelerate electrons or by stripping electrons from thin-foil targets to accelerate ions.  \n© The Author(s), 2023 . Published by Cambridge University Press in association with Chinese Laser Pre","cbCaiu6qKARdP5Rt","https://ap.wps.com/l/cbCaiu6qKARdP5Rt","pdf",10608697,3,1,41,"English","en",105,"# Introduction\n## Laser–plasma physics\n# Laser–plasma acceleration (LPA)\n## Laser wakefield acceleration (LWFA)","[{\"question\":\"Why has data-driven machine learning become important in laser–plasma physics?\",\"answer\":\"Because modern experiments and simulations can collect data from hundreds or thousands of different settings, enabling use of mathematics, statistics, and computer-science methods for big data. Modeling approaches also help when data are sparse.\"},{\"question\":\"What are the main application subfields highlighted for machine learning methods?\",\"answer\":\"The paper focuses on laser-plasma acceleration and inertial confinement fusion as key laser–plasma physics subfields where machine learning methods can be applied.\"},{\"question\":\"How does laser-plasma acceleration accelerate particles according to the paper?\",\"answer\":\"It accelerates charged particles to high energies over short distances by inducing charge separation in plasma, such as plasma waves to accelerate electrons or stripping electrons from thin-foil targets to accelerate ions.\"}]","Data-driven science and machine learning methods in laser–plasma physics | PDF",1785933447,103,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"data-driven-science-and-machine-learning-methods-in-laserplasma-physics","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/data-driven-science-and-machine-learning-methods-in-laserplasma-physics/126577/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-28","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why has data-driven machine learning become important in laser–plasma physics?","Question",{"text":76,"@type":77},"Because modern experiments and simulations can collect data from hundreds or thousands of different settings, enabling use of mathematics, statistics, and computer-science methods for big data. Modeling approaches also help when data are sparse.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What are the main application subfields highlighted for machine learning methods?",{"text":81,"@type":77},"The paper focuses on laser-plasma acceleration and inertial confinement fusion as key laser–plasma physics subfields where machine learning methods can be applied.",{"name":83,"@type":74,"acceptedAnswer":84},"How does laser-plasma acceleration accelerate particles according to the paper?",{"text":85,"@type":77},"It accelerates charged particles to high energies over short distances by inducing charge separation in plasma, such as plasma waves to accelerate electrons or stripping electrons from thin-foil targets to accelerate ions.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]