Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 7 Issue 2
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Artificial Intelligence in Modern Physics: Transforming Scientific Discovery and Physical Systems

Author(s) Prof. Dr. Harsha Jalori
Country India
Abstract Artificial Intelligence (AI) has emerged as one of the most transformative technological developments of the twenty-first century. Its integration into scientific research has significantly enhanced the ability of researchers to analyse large datasets, simulate complex systems, and develop predictive models. Physics, which deals with fundamental laws governing matter and energy, increasingly relies on computational tools due to the complexity and scale of modern experiments. The application of artificial intelligence techniques such as machine learning, neural networks, and deep learning has opened new possibilities for accelerating scientific discovery in physics.

Modern physics experiments generate enormous volumes of data from particle accelerators, astronomical observatories, and high-precision laboratory instruments. Traditional analytical methods often struggle to interpret such large datasets efficiently. AI provides advanced computational techniques that allow physicists to identify hidden patterns, classify complex events, and optimize experimental processes.

This research paper examines the role of artificial intelligence in physics and explores its applications in particle physics, astrophysics, quantum mechanics, and materials science. The study also analyses methodological approaches used in AI-driven physics research, discusses advantages and limitations, and evaluates future prospects of integrating artificial intelligence with physical sciences. The findings suggest that AI has become an indispensable tool for modern physics research and will continue to revolutionize scientific discovery in the coming decades.
Keywords Artificial Intelligence, Machine Learning, Computational Physics, Neural Networks, Quantum Simulation, Scientific Data Analysis
Field Physical Science
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-04-06
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E-ISSN 3048-7641
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