Artificial Intelligence in Physics: “The Influence is Huge”

All innovative ways of learning are aimed at enabling the average student to learn to think like an expert, to use his/her knowledge like an expert. Traditional physical education, like all-natural sciences, involves the transfer of information to students in lectures, and its consolidation in practical and laboratory classes and in the form of independent homework. At the same time, several aspects of learning are distinguished: conceptual understanding, direct transfer of information, knowledge and basic physical laws. A general drawback of traditional concepts is the low digestibility of the material, which is related to the psychological characteristics of a person: 10% are able to formulate the main ideas of the material that was taught 15 minutes after the explanation, if it is new material for them. All modern educational technologies using interactive methods and various pedagogical methods are aimed at changing the student's psychology and are called upon in various ways and trajectories to reach the sixth level in Bloom's taxonomy - the level of creativity, expert, specialist.
 The ability to solve problems in physics is an important element in the system of physical education, because it allows you to achieve a number of goals: students see the practical application of the acquired theoretical knowledge, which makes the learning process more conscious and changes the attitude to learning; contributes to the development of logical thinking, concretization of knowledge, which connects the theoretical lecture material with its practical application. In the process of solving physics problems, a number of personal abilities develop: mental, creative, logical, intelligence, observation, independence and accuracy.
 The integration of artificial intelligence (AI) generative models GPT in solving physical problems has attracted considerable attention this year. This article examines the complex interaction between AI and student decision-making, shedding light on the cognitive and emotional factors that must be considered when using AI to solve physical tasks. In addition, the pedagogical implications of incorporating AI into physics education are explored, emphasizing the importance of maintaining a balanced approach that promotes the development of critical thinking, creativity, and ethical reasoning. By diligently addressing these challenges, we can harness the potential of AI to expand problem-solving capabilities while preserving the undeniable value of human intelligence and expertise in scientific research

In recent years, the continuous development of big data, cloud services, Internet+, artificial intelligence, and other technologies has accelerated the improvement of data communication services in the traditional pharmaceutical industry. It plays a leading role in the development of my country's pharmaceutical industry, deepening the reform of the health system, improving the efficiency and quality of medical services, and developing new technologies. In this context, we make the following research and draw the following conclusions: (1) the scale of my country's medical big data market is constantly increasing, and the global medical big data market is also increasing. Compared with the global medical big data market, China's medical big data has grown at a faster rate. From the initial 10.33% in 2015, the proportion has reached 38.7% after 7 years, and the proportion has increased by 28.37%. (2) Generally speaking, urine is mainly slightly acidic, that is, the pH is around 6.0, the normal range is 5.0 to 7.0, and there are also neutral or slightly alkaline. 8 and 7.5 are generally people with some physical problems. In recent years, the pharmaceutical industry has continuously developed technologies such as big data, cloud computing, Internet+, and artificial intelligence by improving data transmission services. As an important strategic resource of the country, the generation of great medical skills and great information is of great significance to the development of my country's pharmaceutical industry and the deepening of the reform of the national medical system. Improve the efficiency and level of medical services, and establish forms and services. Accelerate economic growth. In this sense, we set out to explore.

The integration of generative artificial intelligence (AI), particularly Large Language Models (LLMs) like OpenAI's ChatGPT and Microsoft's Copilot, is transforming educational methodologies, including undergraduate physics courses for engineering students. Despite their potential, these LLMs typically rely on statistical learning methods and often exhibit algebraic inaccuracies in solving standard university-level physics problems. This study explores the use of LLMs in physics courses for N = 91 freshman engineering students over two academic terms (Spring and Fall 2023). Students engaged in AI-assisted activities to solve physics problems and were asked to identify and correct the errors made by the chatbot. The outcomes were compared with those from traditional teaching methods without AI involvement, and no significant difference in student learning gains was found. To assess the impact of AI tools in education, a more detailed approach using pre-test and post-test instruments with control and experimental groups is necessary. Survey results revealed, however, that AI-assisted sessions enhanced student engagement, problem-solving skills, and understanding of physics concepts. Students also indicated a strong preference for AI-assisted activities, citing increased motivation and a firm belief in the educational benefits of using these tools. Our findings suggest that well-designed AI interventions can effectively complement traditional instructional methods, especially when the LLMs are integrated with symbolic computational tools like WolframAlpha to improve their accuracy.

The study of matter and energy, as well as their relationships with one another, is the focus of the scientific field known as physics. It is possible to describe physics as the study of nature or as that has been belonging to natural things. This branch of science is concerned with the laws and characteristics of matter, in addition to the forces that act upon it. Physics is often recognized as one of the most challenging scientific disciplines-because, it draws concepts and ideas from other academic subfields, such as biology and chemistry. At the beginning of physics, mathematical models had to be meticulously compiled and then evaluated manually. Scientists are now capable of simulating and solving difficult physics problems with notably more speed, precision, and creativity than ever before because of breakthroughs in artificial intelligence and machine learning. Frameworks powered by artificial intelligence are speeding up the research in a wide variety of fields of physics such as nuclear technology, windmill energy production, thermal power plant, space research and energy management. The application of artificial intelligence for the development of new models and solutions for challenging physics problems has the potential to significantly accelerate the rate of progress of scientific advancement across the most basic field of physics.

The integration of artificial intelligence (AI) in education has significantly transformed learning environments, particularly through the use of large language models (LLMs) such as ChatGPT. While these tools show promise in supporting science and technology education, their effectiveness in solving domain-specific problems, such as Newtonian mechanics, remains under-explored. This study aims to evaluate the capability of ChatGPT in solving essay-type physics problems involving Newton’s Laws of Motion, with a specific focus on force analysis. Using a content-based qualitative evaluation method, the research was conducted in three stages: development and validation of conceptual physics problems, submission of these problems to ChatGPT, and assessment of the AI-generated responses by expert reviewers. The problem used in this study required decomposition of forces on an inclined plane under idealized, frictionless conditions. ChatGPT's responses were evaluated across three dimensions: scientific accuracy, logical coherence, and contextual relevance. The findings indicate that while ChatGPT was able to provide structured and numerically accurate responses, it lacked depth in reasoning and failed to explicitly articulate physical assumptions and validation steps, such as analyzing counteracting gravitational forces. These limitations point to the model's partial conceptual understanding and highlight the need for human oversight. The study concludes that ChatGPT holds potential as a supplementary learning aid, particularly for reinforcing procedural knowledge. However, its use must be carefully integrated into instructional contexts that promote critical thinking and conceptual verification. Recommendations are offered for its pedagogical implementation, along with a call for further research into AI's role in physics education.

Studies show that artificial intelligence (AI) with embedded physics solvers has improved the accuracy of predictions on various physics problems, especially those associated with fluid dynamics. The crucial element in optimizing weight training for estimating flow fields within the AI network lies in the choice of the loss function. In addressing regression-type problems, particularly those involving the temporal evolution of flow fields, the mean square error (MSE) loss function is commonly employed at the current and single time step. However, an issue arises in existing methodologies that utilize MSE-based loss functions with single-time step information for predicting unsteady flow. Most of these approaches overlook the significance of incorporating the temporal history of the flow, a factor that cannot be disregarded in the context of numerical solvers. Hence, in this work, a physics-based AI (PbAI) method with higher-order loss functions is applied to unsteady scenarios, in particular to two distinct turbulent flows where a multitude of fine structures is present, namely, forced and decaying turbulence. Direct numerical simulations on uniform Cartesian grids are conducted to simulate these scenarios, generating two distinct datasets for training and inference. Each dataset comprises 32 randomly initialized conditions spanning 4, 848 time steps for each turbulent flow type. Five distinct models are devised, incorporating features such as rollouts from coarse numerical solvers and temporal considerations in the loss function calculation. The constructed PbAI models demonstrate consistent improvements in predictive performance over the entire temporal domain. These findings are further corroborated through vorticity correlation analyses. The empirical result demonstrates that the accuracy of the baseline case improves by up to 48% and 30% for forced and decaying turbulence, respectively. These results significantly underscore the importance of the temporal histories of flow in the loss function in enhancing predictive capabilities for complex and unsteady turbulent flows.

Large Language Models (LLMs) have grabbed significant attention from diverse technical fields due to their impressive performance on a variety of Natural Language Processing (NLP) tasks. Although these models excel in various generative tasks, they lack the robust reasoning ability required to solve complex mathematics and physics problems. Despite their inherent limitations, Generative Artificial Intelligence (AI) based chatbots, powered by these large language models, are being rapidly adopted by students in physics and other technical fields. In this project, we assessed the ability of various generative AI-based models to solve Physics problems. We asked currently popular AI models to solve Physics questions from a final board exam of class 12 of the Higher Secondary Education Board (HSEB) of Nepal. We then evaluated the AI-written solutions by the subject matter experts. We found that the gpt-4o model by OpenAI performed the best, securing 90% among the models studied. In this paper, we provide a brief overview of these models and compare their performance as evaluated by a University Physics professor. We will also discuss the risks and benefits of their use in higher education.

The article discusses the features of using artificial intelligence tools in problems of physics, technical means and flaw detection. Prospects for using the results of artificial intelligence for diagnosing technical systems and devices used in transport, from the point of view of monitoring their residual life with given probabilities of errors of the first and second types. The legal aspects of the use of artificial intelligence in routine maintenance carried out at transport facilities are considered.

<sec><p indent="0mm">The 2024 Nobel Prize in Physics was awarded to John Hopfield and Geoffrey Hinton for their pioneering contributions to artificial neural networks and machine learning. Hopfield was originally trained as a condensed matter physicist, while Hinton has a background in cognitive psychology and artificial intelligence. Both of them recognized the deep connection between neural computation and statistical physics. Their pioneering work demonstrates how principles from statistical physics shaped the theoretical foundations of artificial neural networks and deep learning. This review mainly introduces their breakthrough achievements in neural networks and machine learning, with particular emphasis on the underlying physical principles. </sec><sec> The Hopfield model is one of the most significant contributions of John Hopfield, introducing a groundbreaking theoretical framework for understanding associative memory in machines. This model operates through an iterative dynamic rule, updating neuron states to minimize an energy function, which takes inspiration from spin glass systems in physics. The energy landscape concept in the Hopfield model provides crucial insights into information storage and retrieval. By demonstrating robust distributed representations, the model has inspired extensive research on attractor dynamics in both artificial neural networks and biological systems, serving as a foundational pillar for modern neural architectures and brain-inspired computing. Beyond this model, Hopfield explored time encoding in neural systems, highlighting the role of synchronized oscillations and providing new perspectives on temporal dynamics in enhancing computational capacity. He also pioneered the critical brain hypothesis, linking neural network dynamics to self-organized criticality. </sec><sec> The Boltzmann machine, developed by Geoffrey Hinton and his collaborators, serves as a key architecture bridging statistical physics and machine learning. In this model, the energy function determines the probability distribution of system states following the Boltzmann distribution, with learning based on maximum likelihood estimation. This foundational work led to subsequent innovations, including restricted Boltzmann machines (RBMs), which streamlined the architecture and improved training efficiency. Hinton further advanced deep learning through deep belief networks (DBNs), which stack RBMs into hierarchical architectures, and the contrastive divergence algorithm, which enhanced RBM training efficiency. Beyond the Boltzmann machine, Hinton pioneered advances in backpropagation, deep autoencoders, and techniques like Dropout, optimizing the training process of deep networks. He introduced t-SNE as a powerful visualization tool for high-dimensional data and developed innovative architectures like capsule networks to address limitations in convolutional networks. His forward-forward algorithm represents another significant advancement in learning mechanisms, highlighting his continuous contributions to artificial intelligence. </sec><sec> The Nobel Prize-winning contributions of Hopfield and Hinton exemplify how physical principles can guide the development of revolutionary computational paradigms. Their work has established a bidirectional interaction between the “Science of AI” and “AI for Science”, accelerating interdisciplinary integration and creating new research paradigms that transcend traditional boundaries. In the future, the integration of statistical physics and machine learning will continue to generate new theoretical frameworks for understanding deep learning systems, while also making it possible to solve complex problems in physics and other scientific fields. </sec>

The history of variational calculus dates back to the late 17th century when Johann Bernoulli presented his famous problem concerning the brachistochrone curve. Since then, variational calculus has developed intensively as many problems in physics and engineering are described by equations from this branch of mathematical analysis. This paper presents two non-classical, distinct methods for solving such problems. The first method is based on the differential transform method (DTM), which seeks an analytical solution in the form of a certain functional series. The second method, on the other hand, is based on the physics-informed neural network (PINN), where artificial intelligence in the form of a neural network is used to solve the differential equation. In addition to describing both methods, this paper also presents numerical examples along with a comparison of the obtained results.Comparingthe two methods, DTM produced marginally more accurate results than PINNs. While PINNs exhibited slightly higher errors, their performance remained commendable. The key strengths of neural networks are their adaptability and ease of implementation. Both approaches discussed in the article are effective for addressing the examined problems.

The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI, pronounced /aI‐faI/) is one of the inaugural NSF AI research institutes (https://iaifi.org). The IAIFI is enabling physics discoveries and advancing foundational AI through the development of novel AI approaches that incorporate first principles from fundamental physics. By combining state‐of‐the‐art research with early career talent and a growing AI + physics community in the Boston area and beyond, the IAIFI is enabling researchers to develop AI technologies to tackle some of the most challenging problems in physics, and transfer these technologies to the broader AI community. Since trustworthy AI is as important for physics discovery as it is for other applications of AI in society, IAIFI researchers are applying physics principles to develop more robust AI tools and to illuminate existing AI technologies. To cultivate human intelligence, the IAIFI promotes training, education, and public engagement at the intersection of physics and AI. In these ways, the IAIFI is fusing deep learning with deep thinking to gain a deeper understanding of our universe and AI.

Uncovering the mechanisms of physics is driving a new paradigm in artificial intelligence (AI) discovery. Today, physics has enabled us to understand the AI paradigm in a wide range of matter, energy, and space-time scales through data, knowledge, priors, and laws. At the same time, the AI paradigm also draws on and introduces the knowledge and laws of physics to promote its own development. Then this new paradigm of using physical science to inspire AI is the physical science of artificial intelligence (PhysicsScience4AI, PS4AI). Although AI has become the driving force for development in various fields, there is still a “black box” phenomenon that is difficult to explain in the field of AI deep learning. This article will briefly review the connection between relevant physics disciplines (classical mechanics, electromagnetism, statistical physics, quantum mechanics) and AI. It will focus on discussing the mechanisms of physics disciplines and how they inspire the AI deep learning paradigm, and briefly introduce some related work on how AI solves physics problems. PS4AI is a new research field. At the end of the article, we summarize the challenges facing the new physics-inspired AI paradigm and look forward to the next generation of artificial intelligence technology. This article aims to provide a brief review of research related to physics-inspired AI deep algorithms and to stimulate future research and exploration by elucidating recent advances in physics.

This paper presents a new approach which uses the tools within artificial intelligence (AI) software libraries as an alternative way of solving partial differential equations (PDEs) that have been discretised using standard numerical methods. In particular, we describe how to represent numerical discretisations arising from the finite volume and finite element methods by pre‐determining the weights of convolutional layers within a neural network. As the weights are defined by the discretisation scheme, no training of the network is required and the solutions obtained are identical (accounting for solver tolerances) to those obtained with standard codes often written in Fortran or C++. We also explain how to implement the Jacobi method and a multigrid solver using the functions available in AI libraries. For the latter, we use a U‐Net architecture which is able to represent a sawtooth multigrid method. A benefit of using AI libraries in this way is that one can exploit their built‐in technologies to enable the same code to run on different computer architectures (such as central processing units, graphics processing units or new‐generation AI processors) without any modification. In this article, we apply the proposed approach to eigenvalue problems in reactor physics where neutron transport is described by diffusion theory. For a fuel assembly benchmark, we demonstrate that the solution obtained from our new approach is the same (accounting for solver tolerances) as that obtained from the same discretisation coded in a standard way using Fortran. We then proceed to solve a reactor core benchmark using the new approach. For both benchmarks we give timings for the neural network implementation run on a CPU and a GPU, and a serial Fortran code run on a CPU.

DarkStar was a Strategic Initiative (FY2021-FY2024) to investigate applications of Artificial Intelligence (AI) and Machine Learning (ML) to scientific problems of complex hydrodynamics, shockwave physics and energetic materials. The research focused on physics and engineering design as a process that can be tremendously accelerated through merging AI with advanced physics simulation on exascale-class platforms, and to experimentally validate this revolutionary new approach through dynamic materials campaigns. A central thread of scientific inquiry was in the application of AI to enable human understanding of how to control hydrodynamic instability (which has impacts to areas such as inertial confinement fusion) via engineering features and time-dependent sources. Motivated by an unfinished line of research started by Dr. Johnny von Neumann, AI-enabled simulation approaches were developed that allowed DarkStar researchers to uncover several ground-breaking discoveries regarding hydrodynamic instability, including how to completely suppress Richtmyer-Meshkov instability (RMI). These S&T discoveries, along with other advances, have shown the way for an entirely new approach to time-dependent problems known as inverse design – the idea that complex systems can be developed directly from a final state that is to be achieved and resolve the initial design via satisfying several constraints simultaneously via AI/ML. Through experimental campaigns conducted across a wide range of facilities in the NNSA complex (the High Explosive Application Facility at LLNL, the Dynamic Compression Sector/Advanced Photon Source at Argonne National Lab, and Special Technologies Laboratory at MSTS) the radical new AI/ML approach to engineering complex material dynamics was verified, establishing a new field of study within the realm of shock physics. As advanced manufacturing capabilities continue to develop, the great importance of inverse design as a means to apply that technology effectively for NNSA missions will feature prominently over this decade. DarkStar has positioned NNSA as a world-leader in this newly emerging cross-disciplinary area of AI methods for advanced physics simulation and pioneered multiple novel approaches that have enabled the broader scientific community. By allowing us to see past the horizon, to 2030 and beyond, DarkStar has illuminated the vast potential of AI/ML to impact a wide range of new national security missions and, consequently, multiple areas of further research have already emerged across the NNSA and DOD complex.

The 2024 Nobel Prizes in Physics and Chemistry were awarded for foundational discoveries and inventions enabling machine learning through artificial neural networks. Artificial intelligence (AI) and artificial metamaterials are two cutting-edge technologies that have shown significant advancements and applications in various fields. AI, with its roots tracing back to Alan Turing’s seminal work, has undergone remarkable evolution over decades, with key advancements including the Turing Test, expert systems, deep learning, and the emergence of multimodal AI models. Electromagnetic wave control, critical for scientific research and industrial applications, has been significantly broadened by artificial metamaterials. This review explores the synergistic integration of AI and artificial metamaterials, emphasizing how AI accelerates the design and functionality of artificial materials, while novel physical neural networks constructed from artificial metamaterials significantly enhance AI’s computational speed and its ability to solve complex physical problems. This paper provides a detailed discussion of AI-based forward prediction and inverse design principles and applications in metamaterial design. It also examines the potential of big-data-driven AI methods in addressing challenges in metamaterial design. In addition, this review delves into the role of artificial metamaterials in advancing AI, focusing on the progress of electromagnetic physical neural networks in optics, terahertz, and microwaves. Emphasizing the transformative impact of the intersection between AI and artificial metamaterials, this review underscores significant improvements in efficiency, accuracy, and applicability. The collaborative development of AI and artificial metamaterials accelerates the metamaterial design process and opens new possibilities for innovations in photonics, communications, radars, and sensing.