Jul 2026· International Research Journal on Advanced Engineering Hub (IRJAEH)· Vol 4, pp. 4992-4998· 0 citations· 10 references
TL;DR
This work firstly focuses on how AI can be leveraged in modern physics and at the same time identifies the key challenges and the possible directions that the AI-physics tandem could take in the future.
Abstract
Artificial Intelligence (AI) is reshaping the field of physics by offering researchers more efficient ways to analyze data, simulate occurrences, and make scientific discoveries. Through machine learning and deep learning, physicists can now handle enormous datasets, both from labs and observations, much faster and with fewer errors than with conventional techniques. AI has touched various areas of physics like particle physics astrophysics quantum physics, and material sciences. Besides helping with particle recognition, it is also used for analyzing gravitational waves, looking for dark matter, and even designing new materials that exhibit extraordinary physical properties. This work firstly focuses on how AI can be leveraged in modern physics and at the same time identifies the key challenges and the possible directions that the AI-physics tandem could take in the future. It details how AI methods dramatically increase research productivity, make it possible to perform intricate simulations at a much faster pace, and help uncover even the subtle patterns and correlations in complex physical systems. The paper enlightened us about new tools being developed like Physics-Informed Neural Networks (PINNs) and generative AI models - these are hybrid approaches that integrate the fundamentals of physics and state-of-the-art computation techniques. As advantageous as AI is, it is not without shortcomings like the dependence on very good quality data, the difficulty in understanding how models make their decisions, the huge computational power they require, and the need to always comply with the laws of physics. Continuing to merge AI and physics could very well lead to a complete transformation of the way we do science, resulting in quicker discovery and deeper understanding of the universe at its core.
Artificial intelligence (AI) and physics are entering a new stage of deep interdisciplinary integration. Physics has long provided foundational ideas for AI, including symmetry, statistical mechanics, and quantum theory, while continuing to inspire new model architectures, learning principles, and computational paradigms. At the same time, as AI systems become increasingly complex, their internal mechanisms, collective behavior, and emergent dynamics are becoming new objects of interest and investigation in physics. In this Perspective, we introduce Physics-first AI, a research paradigm that regards AI as a complex information-processing system embedded in the physical world and seeks to understand, design, and build intelligent systems from fundamental physics principles and modes of reasoning. Physics-First AI aims to provide a common foundation for model architectures, training mechanisms, computational processes, and reasoning paradigms through a unified physics perspective. We trace the historical interplay between physics and AI and discuss how structure priors, statistical mechanics, quantum theory, and physics reasoning may together shape the next generation of intelligent systems. We further examine how this emerging paradigm could reshape physics education and the training of interdisciplinary researchers in the AI era. Physics-First AI offers a route toward a physics-grounded theory and design framework for next-generation intelligence, opening new opportunities for scientific AI, autonomous scientific discovery, and ultimately artificial general intelligence.
Unknown authors· Physics and Engineering· 0 citations
Particle physics experiments have long research cycles and high barriers to entry. Limited human resources make it difficult to fully explore and analyze large volumes of collision data. Agents based on large language models can write code, retrieve literature, and carry out multi-step analyses, offering an opportunity to address this challenge. This article focuses on the Just Furnish Context (JFC) agent framework and examines results produced by the framework, including the CMS H→τ+τ- signal-strength measurement and the ALEPH Lund jet-plane density measurement, as well as their implications for particle physics research. It also summarizes the framework's current limitations and discusses prospects for benchmark-dataset construction, data processing at next-generation large-scale scientific facilities, and the training of future physicists.
Unknown authors· Physics and Engineering· 0 citations
The sections that follow trace the historical development of AI within computer science, review the principal technique families and their applications, and close with an original discussion of cross-cutting patterns, ethical obligations, and likely future directions.
Elayaraja Subbaiah, Manykandaprebou Vaitinadin, E. Kesavan· International Journal of Sci...· 0 citations
Scientific AI is rapidly transforming fundamental physics research and challenging defining aspects of the fundamental physics methodology. We discuss opportunities and dangers of this transformation and find exciting benefits from a close interaction between AI and fundamental physics, provided that we remain aware of the scientific methodologies of the respective fields. For fundamental physics, we discuss two such aspects: statistical validation and a generalizing theory description, both with the goal of discovering new physics in vast datasets.
Michael Krämer, T. Plehn· The European Physical Journa...· 2 citations
AI is accelerating physics discovery, but perhaps away from Einstein-level theory building. To understand this gap, we must recognize a striking trend: while being very successful, the most visible AI contributions to physics discovery appear to mirror the historical development of physics, but in reverse. Human discovery in physics progressed, in broad strokes, from ancient pattern prediction, through phenomenological laws such as Kepler's, to principle-based universal theories such as relativity and the Standard Model. On the AI side, prominent contributions to physics discovery point in the opposite direction: early milestones emphasized explicit equation-discovery methods, such as symbolic regression, whereas more recent frontier contributions are powerful predictors such as AlphaFold and GraphCast, which can be remarkably accurate yet do not provide clear theoretical understanding. If this trend continues, AI would become extraordinarily good at prediction but may struggle to ever propose its first serious contender to quantum gravity or other paradigm-level theories. We review the current landscape of AI for physics discovery and highlight a critical missing skill: the ability to pose the right questions or invent the right principles to guide the development of new theories and the tests to falsify them. This mode of discovery has driven many of the deepest advances since the 17th century, where symmetry, simplicity, and new mathematical frameworks guided theory construction before experimental tests. Equipping AI systems with such skills could move them from predicting within known frameworks to proposing the next paradigm-level discovery in physics.
M. Shalyt, Nathan Regev, Marin Soljačić et al.· arXiv.org· 0 citations
Finding new physics (NP) is the most important problem in particle physics today. Studying ``anomalies'', i.e., measurements of low-energy observables whose values disagree with the predictions of the Standard Model (SM), is a powerful search strategy. The SM Effective Field Theory (SMEFT) provides a general model-independent framework for parameterizing NP; it is natural to try to find the SMEFT operator(s) that can explain such anomalies. This is a challenging task because (i) the number of SMEFT operators is enormous, and (ii) at loop level there are very complicated correlations among the operators. Analyses by humans typically rely on phenomenological intuition to decide which operators are relevant. This is often biased and does not explore the complete SMEFT operator space. Interestingly, reinforcement learning (RL) techniques excel at tasks that require decision making to achieve their goals. In this paper, we introduce an RL method that can be used to find the SMEFT operators that explain any anomalies. We test it on the CDF $W$-mass anomaly, and show that it reproduces (and improves upon) known results. We then consider a far more complicated situation with multiple anomalies and show that, even here, this method is able to find the SMEFT operators that explain the data. Our RL method can therefore be used to efficiently search for NP at the level of SMEFT.
Jacky Kumar, Marianne Bouchard, David London· 0 citations
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