[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116857-en":3,"doc-seo-116857-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116857,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Exploring QCD Matter in Extreme Conditions with Machine Learning","Machine learning has become a powerful computational approach for studying strongly interacting QCD matter under extreme conditions. This review surveys the current state of applying machine learning to theoretical high energy nuclear physics, covering heavy ion collisions, lattice field theory, and neutron stars. It compares methodology driven by data with physics driven modeling, and emphasizes how incorporating physics priors improves purely data-driven learning. The article concludes with key challenges and future directions.","arXiv :2303 . 15136v1 [hep-ph] 27 Mar 2023  \nExploring QCD matter in extreme conditions with Machine Learning  \nKai Zhoua,􀀃, Lingxiao Wanga,􀀃, Long-Gang Pangb,􀀃, Shuzhe Shic,􀀃  \na Frankfurt Institute for Advanced Studies (FIAS), D-60438 Frankfurt am Main, Germany.  \nb Institute of Particle Physics and Key Laboratory of Quark and Lepton Physics (MOE), Central China Normal University, Wuhan, 430079,  \nChina.  \nc Center for Nuclear Physics, Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY 11794-3800, USA.  \nAbstract  \nIn recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This review article aims to provide an overview of the current state of this intersection of fields, focusing on the application of machine learning to theoretical studies in high energy nuclear physics. It covers diverse aspects, including heavy ion collisions, lattice field theory, and neutron stars, and discuss how machine learning can be used to explore and facilitate the physics goals of understanding QCD matter. The review also provides a commonality overview from a methodology perspective, from data-driven perspective to physics-driven perspective. We conclude by discussing the challenges and future prospects of machine learning applications in high energy nuclear physics, also underscoring the importance of incorporating physics priors into the purely data-driven learning toolbox. This review highlights the critical role of machine learning as a valuable computational paradigm for advancing physics exploration in high energy nuclear physics. Keywords: machine learning, heavy ion collisions, lattice QCD, neutron star, inverse problem  \n􀀃 Corresponding authors  \nEmail addresses: [zhou@fias.uni-frankfurt.de](zhou@fias.uni-frankfurt.de) (Kai Zhou), [lwang@fias.uni-frankfurt.de](lwang@fias.uni-frankfurt.de) (Lingxiao Wang), [lgpang@ccnu.edu.cn](lgpang@ccnu.edu.cn)  \n(Long-Gang Pang), [shuzhe.shi@stonybrook.edu](shuzhe.shi@stonybrook.edu) (Shuzhe Shi)  \nPreprint submitted to Elsevier March 28, 2023  \nContents  \n1 Introduction 3  \n1.1 Background ..................................................... 4  \n1.2 Machine Learning in a Nutshell .......................................... 6  \n1.2.1 Bayesian Inference ............................................. 6  \n1.2.2 Deep Learning ............................................... 8  \n1.2.3 Generative Models ............................................. 11  \n1.2.4 Physics-motivated New Developments .................................. 15  \n1.3 Outline ....................................................... 16  \n2 Heavy-Ion Collisions 17  \n2.1 Overview and Challenges for HICs ........................................ 17  \n2.1.1 “Standard Model” of Simulating HICs .................................. 17  \n2.1.2 HIC Challenges ............................................... 21  \n2.2 Initial States and Collision Geometry ....................................... 22  \n2.2.1 Impact Parameter Determination ..................................... 22  \n2.2.2 Unsupervised Centrality Outlier Detection ................................ 24  \n2.2.3 Nuclear Structure Inference ........................................ 25  \n2.3 QCD Phase Diagram ................................................ 26  \n2.3.1 Bayesian Analysis of QCD EoS at 􀀖 B = 0 ................................ 26  \n2.3.2 Identify QCD Phase Transitions using CNN ............................... 27  \n2.3.3 Learning Stochastic Process with QCD Phase Transition ........................ 30  \n2.3.4 Point Cloud Network for Spinodal Clumping Identification ...................... 31  \n2.3.5 Dynamical Edge Convolution Network for Critical Similarity ..................... 32  \n2.3.6 Active Learning for Unstable Regions in QCD EoS ........................... 34  \n2.4 Dynamical Properties of QCD","cbCaidFUIktY1pWC","https://ap.wps.com/l/cbCaidFUIktY1pWC","pdf",14692323,1,146,"English","en",105,"# Introduction\n## Background\n## Machine Learning in a Nutshell\n## Outline\n# Heavy-Ion Collisions\n## Overview and Challenges for HICs\n## Initial States and Collision Geometry\n## QCD Phase Diagram\n## Dynamical Properties of QCD Matter\n## Fast Simulations for HICs\n## Summary\n# Lattice QCD\n## Overview and Challenges in Lattice Field Theory\n## Field Configuration Generation\n## Observables Analysis for QFT\n## Sign Problem\n## Summary","[{\"question\":\"How does the review position machine learning in high energy nuclear physics?\",\"answer\":\"It presents machine learning as a computational tool and a new problem-solving perspective for investigating properties of strongly interacting QCD matter under extreme conditions.\"},{\"question\":\"Which application areas are covered for machine learning in QCD research?\",\"answer\":\"The review covers heavy ion collisions, lattice field theory, and neutron stars, explaining how machine learning supports physics goals in each area.\"},{\"question\":\"What is the methodology focus of the review regarding data-driven vs physics-driven approaches?\",\"answer\":\"It provides a commonality overview from both data-driven and physics-driven perspectives, highlighting the importance of adding physics priors to data-driven learning tools.\"}]","Exploring QCD Matter in Extreme Conditions with Machine Learning | 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does the review position machine learning in high energy nuclear physics?","Question",{"text":75,"@type":76},"It presents machine learning as a computational tool and a new problem-solving perspective for investigating properties of strongly interacting QCD matter under extreme conditions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which application areas are covered for machine learning in QCD research?",{"text":80,"@type":76},"The review covers heavy ion collisions, lattice field theory, and neutron stars, explaining how machine learning supports physics goals in each area.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the methodology focus of the review regarding data-driven vs physics-driven approaches?",{"text":84,"@type":76},"It provides a commonality overview from both data-driven and physics-driven perspectives, highlighting the importance of adding physics priors to data-driven learning 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