Relevant bibliographies by topics / Artificial Intelligence in Physics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Artificial Intelligence in Physics'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Artificial Intelligence in Physics"

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Jalilova, S., and G. Musayeva. "ARTIFICIAL INTELLIGENCE IN PHYSICS TEACHING." Sciences of Europe, no. 157 (January 27, 2025): 46–49. https://doi.org/10.5281/zenodo.14744941.

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This paper presents a comprehensive literature review on the application of artificial intelligence in physics teaching. The purpose of the study is to explore the fundamental concepts of AI, its various applications in physics teaching, and the benefits and challenges associated with its implementation. Through a systematic search of academic databases, a collection of relevant research articles, journals, conference proceedings and books on the use of AI in physics education was assembled. The selected studies were analyzed and synthesized to develop a coherent framework for understanding th
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Pattnaik, P. C., G. Fletcher, and J. L. Fry. "Artificial intelligence programming in physics." European Journal of Physics 7, no. 1 (1986): 25–28. http://dx.doi.org/10.1088/0143-0807/7/1/005.

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Chougule, Arun, and Gourav Kumar Jain. "Artificial Intelligence in Medical Physics." Journal of Medical Physics 49, no. 3 (2024): 489–91. http://dx.doi.org/10.4103/jmp.jmp_102_24.

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Trout, Joseph J., and Lauren Winterbottom. "Artificial intelligence and undergraduate physics education." Physics Education 60, no. 1 (2024): 015024. https://doi.org/10.1088/1361-6552/ad98de.

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Abstract The latest advances in science and technology have resulted in great advances in artificial intelligence (AI), including the creation of chatbots. Chatbots simulate human conversation and allow humans to ask questions and receive answers based on a large volume of electronically stored information. Faculties of universities around the world are trying to come to grips with the availability of AI tools, such as chatbots, and are debating the ethical and moral questions surrounding the use of AI in education. This paper presents the results of a study which intended to answer three rese
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Sperling, Alissa, and James Lincoln. "Artificial intelligence and high school physics." Physics Teacher 62, no. 4 (2024): 314–15. http://dx.doi.org/10.1119/5.0202994.

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Jing, Yumei, and Fangping Ouyang. "The Role of Integrating Artificial Intelligence and Virtual Simulation Technologies in Physics Teaching." Advances in Education, Humanities and Social Science Research 6, no. 1 (2023): 572. http://dx.doi.org/10.56028/aehssr.6.1.572.2023.

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With the continuous progress of information technology, the integration of artificial intelligence and virtual simulation technologies into the teaching system is of great significance to promote the informatization reform of physics teaching and the cultivation of innovative talents. This paper first introduces the characteristics of the combination of artificial intelligence and virtual simulation technologies, then comprehensively analyzes the positive effects of the integration of artificial intelligence and virtual simulation technologies on physics teaching, and finally puts forward some
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Commissariat, Tushna. "Artificial intelligence." Physics World 34, no. 5 (2021): 17. http://dx.doi.org/10.1088/2058-7058/34/05/24.

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Oh, Sang Hoon. "Nobel Prize and Artificial Intelligence." Liberal Arts Innovation Center 17 (March 31, 2025): 1061–81. https://doi.org/10.54698/kl.2025.17.1061.

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The Nobel Prize in science has traditionally been awarded to researchers who have made groundbreaking discoveries or inventions that significantly contribute to human progress in the field of pure science. However, in 2024, the Nobel Prize in Physics was awarded not to a researcher in pure physics but to a researcher in the field of artificial intelligence. Similarly, the Nobel Prize in Chemistry was awarded not to a pure chemistry researcher but to a scientist studying protein structures using artificial intelligence or computational techniques. This paper examines the research achievements o
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Allen, Machel M. A. "Physics-Based Transfer Learning and Artificial Intelligence." International Journal of Advanced Engineering Research and Science 7, no. 8 (2020): 266–74. http://dx.doi.org/10.22161/ijaers.78.28.

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Zanca, F., M. Avanzo, N. Colgan, et al. "Focus issue: Artificial intelligence in medical physics." Physica Medica 83 (March 2021): 287–91. http://dx.doi.org/10.1016/j.ejmp.2021.05.008.

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Dissertations / Theses on the topic "Artificial Intelligence in Physics"

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Peurifoy, John Edward. "The physics of artificial intelligence." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/122844.

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Thesis: S.B., Massachusetts Institute of Technology, Department of Physics, 2018<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 83-87).<br>In this thesis, I explore both what Physics can lend to the world of artificial intelligence, and how artificial intelligence can enhance the world of physics. In the first chapter I propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. This neural network model is experimentally shown to describe the system well, and is then further used to solve the inverse d
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Leach, Andrew Richard. "The application of artificial intelligence techniques in conformational analysis." Thesis, University of Oxford, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.314898.

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Bains, Sunny Rukhsana Elynn. "Physical computation and embodied artificial intelligence." Thesis, Open University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.409862.

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Stoyko, Darryl Keith. "Using the singularity frequencies of guidedwaves to obtain a pipe's properties and detect and size notches." Thesis, University of Manitoba (Canada), 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3582897.

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<p> A survey of relevant literature on the topic of wave propagation and scattering in pipes is given first. This review is followed by a theoretical framework which is pertinent to wave propagation in homogeneous, isotropic, pipes. Emphasis is placed on approximate solutions stemming from a computer based, <u>S</u>emi-<u>A</u>nalytical <u> F</u>inite <u>E</u>lement (SAFE) formulation. A modal analysis of the dynamic response of homogeneous, isotropic pipes, when subjected to a transient ultrasonic excitation, demonstrates that dominant features, i.e., singularities in an unblemished pipe&rsqu
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Dye, Brian Christopher. "Identifying Strombolian Eruptions through Cross-Correlation of Seismic Data and Machine Learning of Infrared, Lava-Lake Images on Mount Erebus, Antarctica." Thesis, University of Louisiana at Lafayette, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10815582.

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<p>Mount Erebus, Antarctica, is a volcano with frequent lava-lake eruptions known as strombolian eruptions. The larger of these eruptions create strong seismic waves and have a characteristic seismic signature that can be analyzed through three-component cross-correlation to distinguish smaller strombolian eruptions from the background noise of the volcano. The addition of an infrared camera on the rim of Mount Erebus allows for the confirmation of strombolian eruptions as opposed to unrelated seismic activity. This research finds that eruption events can also be detected categorizing the imag
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Brown, Stephen F. (Stephen Francis). "The Use of Learning Theory in the Application of Artificial Intelligence to Computer-Assisted Instruction of Physics." Thesis, North Texas State University, 1985. https://digital.library.unt.edu/ark:/67531/metadc330775/.

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It was the purpose of this research, to develop and test an artificially intelligent, learner-based, computer-assisted physics tutor. The resulting expert system is named ARPHY, an acronym for ARtificially intelligent PHYsics tutor. The research was conducted in two phases. In the first phase of the research, the system was constructed using Ausubel's advance organizer as a guiding learning theory. The content of accelerated motion was encoded into this organizer after sub-classification according to the learning types identified by Gagnds. The measurement of the student's level of learning wa
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Morgan, A. J. "The qualitative behaviour of dynamic physical systems." Thesis, University of Cambridge, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.233967.

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Qualitative representations concentrate on general behaviours rather than numerical accuracy. This thesis introduces methods for producing qualitative descriptions of dynamically changing quantities. A distinction is made between scalar and vector representations of quantities, and several qualitative vector operations are defined, including a qualitative calculus. These operations correspond closely to their normal numerical counterparts. A systematic approach to a model-based method is presented for the analysis of physical systems, which allows the derivation of behaviour for a range of ope
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Palitawanont, Nanta. "An Investigation into the Effectiveness of Intelligent Tutoring on Learning of College Level Statistics." Thesis, University of North Texas, 1989. https://digital.library.unt.edu/ark:/67531/metadc331166/.

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The present research incorporated the content of basic statistics into the Artificial Intelligence Physics Tutor (ARPHY), which was used as the expert system shell, and investigated the effects of the Artificial Intelligent Statistics Tutor (ARSTAT) as a supplement to learning statistics at the college level. Two classes of an introductory educational statistics course in the Department of Educational Foundations, University of North Texas, were used in the study. The daytime class was used as the experimental group and the evening class was used as the control group. The experimental group's
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Lagula, Daniel, and Filip Karlsson. "Comparison of Two Different Methods of Generating Physics-Based Character Animation using Reinforcement Learning." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20227.

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In the area of Physics-Based Animation using Reinforcement Learning (RL), multiple games and virtual simulations have had it as an objective throughout the years because of the added realism it brings to motions of characters. It also brings a realistic and practical interaction between the characters and their environment. By using a physically simulated ragdoll and Reinforcement Learning, the ragdoll can learn how to walk and balance. This has the potential of bringing realistic and interactive real-time animations, of normally rigid animations, to life. No longer do animators have to animat
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Saunders, Brandon Scot. "Observational Intelligence: An Overview of Computational Actual Entities and their Use as Agents of Artificial Intelligence." VCU Scholars Compass, 2007. http://scholarscompass.vcu.edu/etd/1235.

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This thesis' focus is on the use of Alfred North Whitehead's concept of Actual Entities as a computational tool for computer science and the introduction of a novel usage of Actual Entities as learning agents. Actual Entities are vector based agents that interact within their environment through a process called prehension. It is the combined effect of multiple Actual Entities working within a Colony of Prehending Entities that produces emergent, intelligent behavior. It is not always the case that prehension functions for desired behavior are known beforehand and frequently the functions a
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Books on the topic "Artificial Intelligence in Physics"

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Ratsch, Ulrich. Intelligence and Artificial Intelligence: An Interdisciplinary Debate. Springer Berlin Heidelberg, 1998.

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Valdes, Gilmer, and Lei Xing. Artificial Intelligence in Radiation Oncology and Biomedical Physics. CRC Press, 2023. http://dx.doi.org/10.1201/9781003094333.

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Boi, Faltings, and Struss Peter, eds. Recent advances in qualitative physics. MIT Press, 1992.

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Delgado-Frias, José G. VLSI for Artificial Intelligence and Neural Networks. Springer US, 1991.

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Majumdar, M. Catherine. Artificial Intelligence and Other Innovative Computer Applications in the Nuclear Industry. Springer US, 1988.

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Baba, Norio. Computational Intelligence in Games. Physica-Verlag HD, 2001.

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Kumar Sagar, Anil, Parma Nand, Neetesh Kumar, Sanjoy Das, and Subrata Sahana. Artificial Intelligence in Cyber-Physical Systems. CRC Press, 2023. http://dx.doi.org/10.1201/9781003248750.

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Zelinka, Ivan. Nostradamus: Modern Methods of Prediction, Modeling and Analysis of Nonlinear Systems. Springer Berlin Heidelberg, 2013.

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Zamojski, Wojciech. New Results in Dependability and Computer Systems: Proceedings of the 8th International Conference on Dependability and Complex Systems DepCoS-RELCOMEX, September 9-13, 2013, Brunów, Poland. Springer International Publishing, 2013.

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Zamojski, Wojciech. Complex Systems and Dependability. Springer Berlin Heidelberg, 2012.

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Book chapters on the topic "Artificial Intelligence in Physics"

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Ince, Elif. "Artificial Intelligence in Nuclear Physics and Its Worldwide Applications." In Artificial Intelligence. CRC Press, 2024. http://dx.doi.org/10.1201/9781003483571-16.

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Mohaghegh, Shahab D. "Modeling Physics Using Artificial Intelligence." In Artificial Intelligence for Science and Engineering Applications. CRC Press, 2024. http://dx.doi.org/10.1201/9781003369356-5.

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Brody, Thomas A. "Epistemological Implications of Artificial Intelligence." In The Philosophy Behind Physics. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78978-6_23.

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Lynch, Stephen. "Physics." In Python for Scientific Computing and Artificial Intelligence. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003285816-14.

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Marquez, Bicky A., Chaoran Huang, Paul R. Prucnal, and Bhavin J. Shastri. "Neuromorphic Silicon Photonics for Artificial Intelligence." In Topics in Applied Physics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68222-4_10.

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Wichert, Andreas. "Quantum Physics and Quantum Computation." In Quantum Artificial Intelligence with Qiskit. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003374404-2.

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D’Ariano, Giacomo Mauro, and Federico Faggin. "Hard Problem and Free Will: An Information-Theoretical Approach." In Artificial Intelligence Versus Natural Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85480-5_5.

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AbstractWe explore definite theoretical assertions about consciousness, starting from a non-reductive psycho-informational solution of David Chalmers's hard problem, based on the hypothesis that a fundamental property of “information" is its experience by the supporting “system". The kind of information involved in consciousness needs to be quantum for multiple reasons, including its intrinsic privacy and its power of building up thoughts by entangling qualia states. As a result we reach a quantum-information-based panpsychism, with classical physics supervening on quantum physics, quantum phy
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Bellinger, Colin, Rory Coles, Mark Crowley, and Isaac Tamblyn. "Reinforcement Learning in a Physics-Inspired Semi-Markov Environment." In Advances in Artificial Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47358-7_6.

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Reifman, Jaques, and Thomas Y. C. Wei. "Systematic construction of qualitative physics-based rules for process diagnostics." In Progress in Artificial Intelligence. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60428-6_26.

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Saeed, Hina, and Issam El Naqa. "Artificial Intelligence in Clinical Trials." In Machine and Deep Learning in Oncology, Medical Physics and Radiology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83047-2_19.

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Conference papers on the topic "Artificial Intelligence in Physics"

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Querlioz, Damien. "Computing with physics: the Bayesian approach." In Emerging Topics in Artificial Intelligence (ETAI) 2024, edited by Giovanni Volpe, Joana B. Pereira, Daniel Brunner, and Aydogan Ozcan. SPIE, 2024. http://dx.doi.org/10.1117/12.3028627.

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Huang, Luzhe, Hanlong Chen, Tairan Liu, and Aydogan Ozcan. "GedankenNet: self-supervised learning of holographic imaging enabled by physics consistency." In Emerging Topics in Artificial Intelligence (ETAI) 2024, edited by Giovanni Volpe, Joana B. Pereira, Daniel Brunner, and Aydogan Ozcan. SPIE, 2024. http://dx.doi.org/10.1117/12.3027298.

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Zhang, Qian, Yuan Sui, Stefan Rothe, and Jürgen W. Czarske. "Learning to decompose multimode fibers using a physics-informed neural network." In Emerging Topics in Artificial Intelligence (ETAI) 2024, edited by Giovanni Volpe, Joana B. Pereira, Daniel Brunner, and Aydogan Ozcan. SPIE, 2024. http://dx.doi.org/10.1117/12.3027588.

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Uhrich, Benjamin, and Erhard Rahm. "MPGT: Multimodal Physics-Constrained Graph Transformer Learning for Hybrid Digital Twins." In 2025 IEEE Conference on Artificial Intelligence (CAI). IEEE, 2025. https://doi.org/10.1109/cai64502.2025.00011.

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Bralin, Amir, Amogh Sirnoorkar, Yiyuan Zhang, and N. Sanjay Rebello. "Mapping the literature landscape of artificial intelligence and machine learning in physics education research." In 2024 Physics Education Research Conference. American Association of Physics Teachers, 2024. http://dx.doi.org/10.1119/perc.2024.pr.bralin.

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Delligatti, Anthony, and Liang Kong. "Integrating Physics and Machine Learning: A Synergistic Approach to Artificial Intelligence Education." In 2024 6th International Workshop on Artificial Intelligence and Education (WAIE). IEEE, 2024. https://doi.org/10.1109/waie63876.2024.00067.

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Daffara, Claudia, Nicole de Manincor, Laura Gazzani, et al. "Artificial intelligence and physics for art diagnostics: first results from “AIPAD” project." In Optics for Arts, Architecture, and Archaeology (O3A) X, edited by Roger Groves and Haida Liang. SPIE, 2025. https://doi.org/10.1117/12.3066653.

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Glosemeyer, Tom, Julian Lich, Jürgen W. Czarske, and Robert Kuschmierz. "Diffuser endoscopy for single-shot fluorescence imaging using physics-informed deep learning." In Emerging Topics in Artificial Intelligence (ETAI) 2024, edited by Giovanni Volpe, Joana B. Pereira, Daniel Brunner, and Aydogan Ozcan. SPIE, 2024. http://dx.doi.org/10.1117/12.3026717.

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Tian, Feng, Weijian Yang, and Ben Mattison. "Physics-aware lensless miniscope for neuronal imaging." In Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences II, edited by Liang Gao, Guoan Zheng, and Seung Ah Lee. SPIE, 2025. https://doi.org/10.1117/12.3042594.

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Goracci, Gilberto, Ivan Agostinelli, and Fabio Curti. "Artificial Intelligence-Based Physics Informed Algorithm for Orbit Determination from Very Short Arcs." In 22nd IAA Symposium on Space Debris, Held at the 75th International Astronautical Congress (IAC 2024). International Astronautical Federation (IAF), 2024. https://doi.org/10.52202/078360-0130.

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Reports on the topic "Artificial Intelligence in Physics"

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Horn, Tanja. Winter School: Applications of Artificial Intelligence to topics in Nuclear Physics. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1843613.

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Neubauer, Mark. Final Report for Award DE-SC0021258 (FAIR4HEP): FAIR Framework for Physics-Inspired Artificial Intelligence in High Energy Physics. Office of Scientific and Technical Information (OSTI), 2025. https://doi.org/10.2172/2513592.

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Razdan, Rahul. Product Assurance in the Age of Artificial Intelligence. SAE International, 2025. https://doi.org/10.4271/epr2025011.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Driven by the vast consumer marketplace, the electronics megatrend has reshaped nearly every sector of society. The advancements in semiconductors and software, originally built to serve consumer demand, are now delivering significant value to non-consumer industries. Today, electronics are making inroads into traditionally conservative, safety-critical sectors such as automotive and aerospace. In doing so, electronics—now further propelled by artificial intelligence—are disrupting the functional safety architectures of
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Perdigão, Rui A. P., and Julia Hall. Augmented Post-Quantum Synergistic Manifold Intelligence for Complex System Dynamics and Coevolutionary Multi-Hazards. Synergistic Manifolds, 2024. https://doi.org/10.46337/241211.

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This work seeks to unveil system dynamic predictability beyond existing data and model horizons, and beyond methodological paradigms in classical physics, machine learning and artificial intelligence, in order to increase awareness and preparedness to predict and tackle complexity and multi-hazards in a changing world, one where the coevolution among humans and nature has been reshaping the structure and functioning of our planet and the multiscale multidomain interactions within. For this purpose, we design and implement our next-generation Information Physical suite of Augmented Post-Quantum
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Perdigão, Rui A. P. Earth System Dynamic Intelligence - ESDI. Meteoceanics, 2021. http://dx.doi.org/10.46337/esdi.210414.

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Earth System Dynamic Intelligence (ESDI) entails developing and making innovative use of emerging concepts and pathways in mathematical geophysics, Earth System Dynamics, and information technologies to sense, monitor, harness, analyze, model and fundamentally unveil dynamic understanding across the natural, social and technical geosciences, including the associated manifold multiscale multidomain processes, interactions and complexity, along with the associated predictability and uncertainty dynamics. The ESDI Flagship initiative ignites the development, discussion and cross-fertilization of
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Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, 2021. http://dx.doi.org/10.46337/210930.

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Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platfor
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Ramakrishnan, Aravind, Fangyu Liu, Angeli Jayme, and Imad Al-Qadi. Prediction of Pavement Damage under Truck Platoons Utilizing a Combined Finite Element and Artificial Intelligence Model. Illinois Center for Transportation, 2024. https://doi.org/10.36501/0197-9191/24-030.

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For robust pavement design, accurate damage computation is essential, especially for loading scenarios such as truck platoons. Studies have developed a framework to compute pavement distresses as function of lateral position, spacing, and market-penetration level of truck platoons. The established framework uses a robust 3D pavement model, along with the AASHTOWare Mechanistic–Empirical Pavement Design Guidelines (MEPDG) transfer functions to compute pavement distresses. However, transfer functions include high variability and lack physical significance. Therefore, as an improvement to effecti
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Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maint
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Perdigão, Rui A. P. Information Physical Artificial Intelligence in Complex System Dynamics: Breaking Frontiers in Nonlinear Analytics, Model Design and Socio-Environmental Decision Support in a Coevolutionary World. Meteoceanics, 2020. http://dx.doi.org/10.46337/200930.

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Pasupuleti, Murali Krishna. Mathematical Modeling for Machine Learning: Theory, Simulation, and Scientific Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv125.

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Abstract Mathematical modeling serves as a fundamental framework for advancing machine learning (ML) and artificial intelligence (AI) by integrating theoretical, computational, and simulation-based approaches. This research explores how numerical optimization, differential equations, variational inference, and scientific computing contribute to the development of scalable, interpretable, and efficient AI systems. Key topics include convex and non-convex optimization, physics-informed machine learning (PIML), partial differential equation (PDE)-constrained AI, and Bayesian modeling for uncertai
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