Revolutionizing physics: a comprehensive survey of machine learning applications
In the context of the 21st century and the fourth industrial revolution, the substantial proliferation of data has established it as a valuable resource, fostering enhanced computational capabilities across scientific disciplines, including physics. The integration of Machine Learning stands as a pr...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-02-01
|
| Series: | Frontiers in Physics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2024.1322162/full |
| _version_ | 1797305257478324224 |
|---|---|
| author | Rahul Suresh Hardik Bishnoi Artem V. Kuklin Atharva Parikh Maxim Molokeev Maxim Molokeev Maxim Molokeev R. Harinarayanan Sarvesh Gharat P. Hiba |
| author_facet | Rahul Suresh Hardik Bishnoi Artem V. Kuklin Atharva Parikh Maxim Molokeev Maxim Molokeev Maxim Molokeev R. Harinarayanan Sarvesh Gharat P. Hiba |
| author_sort | Rahul Suresh |
| collection | DOAJ |
| description | In the context of the 21st century and the fourth industrial revolution, the substantial proliferation of data has established it as a valuable resource, fostering enhanced computational capabilities across scientific disciplines, including physics. The integration of Machine Learning stands as a prominent solution to unravel the intricacies inherent to scientific data. While diverse machine learning algorithms find utility in various branches of physics, there exists a need for a systematic framework for the application of Machine Learning to the field. This review offers a comprehensive exploration of the fundamental principles and algorithms of Machine Learning, with a focus on their implementation within distinct domains of physics. The review delves into the contemporary trends of Machine Learning application in condensed matter physics, biophysics, astrophysics, material science, and addresses emerging challenges. The potential for Machine Learning to revolutionize the comprehension of intricate physical phenomena is underscored. Nevertheless, persisting challenges in the form of more efficient and precise algorithm development are acknowledged within this review. |
| first_indexed | 2024-03-08T00:22:21Z |
| format | Article |
| id | doaj.art-d7b6b69f2ab145cca8f0abe3ba275813 |
| institution | Directory Open Access Journal |
| issn | 2296-424X |
| language | English |
| last_indexed | 2024-03-08T00:22:21Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj.art-d7b6b69f2ab145cca8f0abe3ba2758132024-02-16T04:55:58ZengFrontiers Media S.A.Frontiers in Physics2296-424X2024-02-011210.3389/fphy.2024.13221621322162Revolutionizing physics: a comprehensive survey of machine learning applicationsRahul Suresh0Hardik Bishnoi1Artem V. Kuklin2Atharva Parikh3Maxim Molokeev4Maxim Molokeev5Maxim Molokeev6R. Harinarayanan7Sarvesh Gharat8P. Hiba9International Research Center of Spectroscopy and Quantum Chemistry─IRC SQC, Siberian Federal University, Krasnoyarsk, RussiaDepartment of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, IndiaDepartment of Physics and Astronomy, Uppsala University, Uppsala, SwedenDepartment of Information Technology, Vishwakarma Institute of Information Technology, Pune, IndiaInternational Research Center of Spectroscopy and Quantum Chemistry─IRC SQC, Siberian Federal University, Krasnoyarsk, RussiaLaboratory of Theory and Optimization of Chemical and Technological Processes, University of Tyumen, Tyumen, RussiaLaboratory of Crystal Physics, Kirensky Institute of Physics, Federal Research Center KSC SB RAS, Krasnoyarsk, RussiaDepartment of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, IndiaCentre for Machine Intelligence and Data Science, Indian Institute of Technology Bombay, Mumbai, IndiaDepartment of Physics, Pondicherry University, Puducherry, IndiaIn the context of the 21st century and the fourth industrial revolution, the substantial proliferation of data has established it as a valuable resource, fostering enhanced computational capabilities across scientific disciplines, including physics. The integration of Machine Learning stands as a prominent solution to unravel the intricacies inherent to scientific data. While diverse machine learning algorithms find utility in various branches of physics, there exists a need for a systematic framework for the application of Machine Learning to the field. This review offers a comprehensive exploration of the fundamental principles and algorithms of Machine Learning, with a focus on their implementation within distinct domains of physics. The review delves into the contemporary trends of Machine Learning application in condensed matter physics, biophysics, astrophysics, material science, and addresses emerging challenges. The potential for Machine Learning to revolutionize the comprehension of intricate physical phenomena is underscored. Nevertheless, persisting challenges in the form of more efficient and precise algorithm development are acknowledged within this review.https://www.frontiersin.org/articles/10.3389/fphy.2024.1322162/fullphysicsmachine learningneural networkdeep learningartificail intelligence (AI) |
| spellingShingle | Rahul Suresh Hardik Bishnoi Artem V. Kuklin Atharva Parikh Maxim Molokeev Maxim Molokeev Maxim Molokeev R. Harinarayanan Sarvesh Gharat P. Hiba Revolutionizing physics: a comprehensive survey of machine learning applications Frontiers in Physics physics machine learning neural network deep learning artificail intelligence (AI) |
| title | Revolutionizing physics: a comprehensive survey of machine learning applications |
| title_full | Revolutionizing physics: a comprehensive survey of machine learning applications |
| title_fullStr | Revolutionizing physics: a comprehensive survey of machine learning applications |
| title_full_unstemmed | Revolutionizing physics: a comprehensive survey of machine learning applications |
| title_short | Revolutionizing physics: a comprehensive survey of machine learning applications |
| title_sort | revolutionizing physics a comprehensive survey of machine learning applications |
| topic | physics machine learning neural network deep learning artificail intelligence (AI) |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2024.1322162/full |
| work_keys_str_mv | AT rahulsuresh revolutionizingphysicsacomprehensivesurveyofmachinelearningapplications AT hardikbishnoi revolutionizingphysicsacomprehensivesurveyofmachinelearningapplications AT artemvkuklin revolutionizingphysicsacomprehensivesurveyofmachinelearningapplications AT atharvaparikh revolutionizingphysicsacomprehensivesurveyofmachinelearningapplications AT maximmolokeev revolutionizingphysicsacomprehensivesurveyofmachinelearningapplications AT maximmolokeev revolutionizingphysicsacomprehensivesurveyofmachinelearningapplications AT maximmolokeev revolutionizingphysicsacomprehensivesurveyofmachinelearningapplications AT rharinarayanan revolutionizingphysicsacomprehensivesurveyofmachinelearningapplications AT sarveshgharat revolutionizingphysicsacomprehensivesurveyofmachinelearningapplications AT phiba revolutionizingphysicsacomprehensivesurveyofmachinelearningapplications |