• Deepti Sharda
• Ritika Bansal
• Navdeep Kaur
• Deeksha Gupta

Artificial intelligence (AI) is increasingly reshaping scientific research, offering data-driven capabilities that complement traditional analytical and computational methods. In physics, AI helps model complex systems, accelerate simulations, and identify patterns that are difficult to derive through conventional approaches. This paper provides a clear, conceptual overview of how AI contributes to physics, with a focus on Physics-Informed Artificial Intelligence (PI-AI)—a class of methods that embeds physical laws and constraints directly into learning models to ensure physically consistent outcomes.

Physics-Informed Artificial Intelligence (PIAI), Scientific Machine Learning, Physics-Guided Neural Networks, Hybrid Modeling, Computational Physics, Uncertainty Quantification.

"PHYSICS-INFORMED ARTIFICIAL INTELLIGENCE: CONCEPTS, APPLICATIONS, AND FUTURE RESEARCH DIRECTIONS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 9, page no.16-22, September-2026, Available :http://www.jetir.org/papers/JETIRHX06003.pdf

"PHYSICS-INFORMED ARTIFICIAL INTELLIGENCE: CONCEPTS, APPLICATIONS, AND FUTURE RESEARCH DIRECTIONS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 9, page no. pp16-22, September-2026, Available at : http://www.jetir.org/papers/JETIRHX06003.pdf