AI-driven Approaches in Theoretical and Applied Physics: A Comprehensive Study

Abstract

Artificial Intelligence (AI) has emerged as a transformative paradigm in modern physics, fundamentally changing the way researchers model complex systems, analyze experimental data, and discover new physical laws. The convergence of machine learning (ML), deep learning (DL), and physics-informed computational methods has significantly accelerated research across theoretical and applied physics (Karniadakis et al., 2021; Zhang et al., 2023). Unlike traditional computational techniques that often require extensive numerical simulations and handcrafted models, AI-based approaches learn complex relationships directly from data while increasingly incorporating physical constraints to improve accuracy and interpretability (Willard et al., 2022). Recent advances in Physics-Informed Neural Networks (PINNs), neural differential equations, graph neural networks, symbolic regression, and generative AI have enabled efficient solutions to challenging problems in quantum mechanics, high-energy physics, cosmology, materials science, climate modeling, and computational physics (Raissi et al., 2019; Chen et al., 2018; Bronstein et al., 2021; Cranmer et al., 2020). AI has also enhanced experimental automation by optimizing instrument control, accelerating data processing, and supporting autonomous scientific discovery (King et al., 2009). Despite these remarkable achievements, several challenges remain, including limited data availability, model interpretability, physical consistency, computational cost, and the generalization of AI models beyond training datasets (Rudin, 2019; Willard et al., 2022). This review provides a comprehensive overview of AI methodologies applied to physics, highlighting their theoretical foundations, practical applications, current limitations, and emerging research directions. The paper concludes by emphasizing the growing importance of hybrid AI-physics models, explainable artificial intelligence, quantum machine learning, scientific foundation models, and autonomous laboratories as the next generation of intelligent scientific research systems.