Title: Artificial Intelligence in Modern Physics Research: Applications, Challenges, and Future Directions
Authors: Deeksha Gupta, Deepti Sharda, Ritika Bansal, Navdeep Kaur
Volume: 10
Issue: 9
Pages: 224-238
Publication Date: 2026/09/28
Abstract:
Recent years have seen a paradigm shift in physics from analytical and theory-driven approach to data-driven technology. The reason behind this transformation lies in the exponential growth in experimental and simulation data generated in various fields of physics. Owing to explosion of heterogenous and high-dimensional data, Artificial Intelligence based techniques have become backbone of research in modern physics to facilitate data analysis, complex problem solving and experimental design. This paper provides a comprehensive discussion on the applications of machine learning across different domains of physics including quantum physics, astrophysics, high-energy physics, condensed matter physics, plasma physics and nuclear physics. This manuscript explores various machine learning and deep learning-based approaches such as Graph Neural Network, Physics-Informed Neural Networks, Generative adversarial models etc. in uncovering features of complex physics systems. The paper outlines the challenges in amalgamation of AI methods with traditional physics frameworks. The discussion also investigates the future possibilities in reshaping field of physics with advancement in AI.