Artificial Intelligence & Machine Learning in Physics
Artificial Intelligence (AI) and Machine Learning (ML) are transforming modern physics by enabling the analysis of vast and complex datasets. In particle physics, ML algorithms help identify rare events in experiments like those at CERN. In astrophysics, AI assists in detecting exoplanets and gravitational waves from telescope data. Neural networks model quantum systems, optimize simulations, and accelerate materials discovery. These tools enhance prediction accuracy, reduce computational cost, and uncover hidden physical patterns, making AI an essential partner in contemporary scientific research. AI and ML also support automated control systems in fusion experiments and space missions, improving precision and safety. As computational power grows, these technologies are expected to play a central role in theoretical modeling and experimental discovery across physics.
Thomas F Ramos
Lawrence Livermore National Laboratory, United States
Ephraim Suhir
Portland State University, United States
Alexander Unzicker
Pestalozzi Gymnasium Munchen, Germany
Thomas J Webster
Brown University, United States
Jon H Brasher
Stelleo Scientific Foundation, United States
Jason Liu
West Windsor-Plainsboro High School North, United States
Tom Lawrence
Ronin Institute of Independent Scholarship 2.0, United KingdomSubmit your abstract Today
Title : Photoaligned azodye nanolayers: New trends for liquid crystal devices
Vladimir Chigrinov, Hong Kong University of Science and Technology, Hong Kong
Title : Using physics to eliminate implant infection in over 25000 patients to date
Thomas J Webster, Brown University, United States
Title : How the Rad Lab helped avert nuclear war
Thomas F Ramos, Lawrence Livermore National Laboratory, United States
Title : Anisotropic stiffness matrix of bed joint mesh-reinforced masonry: A numerical homogenization approach
Omar Mohammed Daud Shakarneh, Novosibirsk State University of Architecture and Civil Engineering, Russian Federation
Title : Global photochemical model CHARM-DE of the Earth’s atmosphere for altitudes 0-130 km
Alexei Krivolutsky, Central Aerological Observatory (CAO), Russian Federation
Title : Enhanced ferromagnetism in carbon dots polyaniline nanocomposite
Paulo Cesar De Morais, University of Brasilia, Brazil