Relevant bibliographies by topics / Physics-guided

Contents

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

Academic literature on the topic 'Physics-guided'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Physics-guided"

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Gutierrez, Mario A., Jack Dvorkin, and Amos Nur. "Stratigraphy-guided rock physics." Leading Edge 21, no. 1 (2002): 98–103. http://dx.doi.org/10.1190/1.1445859.

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Eaton, D. J. "Physics Support of Image-guided Radiotherapy." Clinical Oncology 25, no. 3 (2013): e31. http://dx.doi.org/10.1016/j.clon.2012.12.003.

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Zhu, Dan, Jiayu Peng, and Cong Ding. "A Neural Network with Physical Mechanism for Predicting Airport Aviation Noise." Aerospace 11, no. 9 (2024): 747. http://dx.doi.org/10.3390/aerospace11090747.

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Airport noise prediction models are divided into physics-guided methods and data-driven methods. The prediction results of physics-guided methods are relatively stable, but their overall prediction accuracy is lower than that of data-driven methods. However, machine learning methods have a relatively high prediction accuracy, but their prediction stability is inferior to physics-guided methods. Therefore, this article integrates the ECAC model, driven by aerodynamics and acoustics principles under the framework of deep neural networks, and establishes a physically guided neural network noise p
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Pawar, Suraj, Omer San, Burak Aksoylu, Adil Rasheed, and Trond Kvamsdal. "Physics guided machine learning using simplified theories." Physics of Fluids 33, no. 1 (2021): 011701. http://dx.doi.org/10.1063/5.0038929.

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Pawar, Suraj, Omer San, Burak Aksoylu, Adil Rasheed, and Trond Kvamsdal. "Physics guided machine learning using simplified theories." Physics of Fluids 33, no. 1 (2021): 011701. http://dx.doi.org/10.1063/5.0038929.

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Keyser, Paul T. "Aristotle's Physics: A Guided Study. Joe Sachs." Isis 87, no. 4 (1996): 716–17. http://dx.doi.org/10.1086/357668.

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Gupta, Kishor Datta, Sunzida Siddique, Roy George, Marufa Kamal, Rakib Hossain Rifat, and Mohd Ariful Haque. "Physics Guided Neural Networks with Knowledge Graph." Digital 4, no. 4 (2024): 846–65. http://dx.doi.org/10.3390/digital4040042.

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Over the past few decades, machine learning (ML) has demonstrated significant advancements in all areas of human existence. Machine learning and deep learning models rely heavily on data. Typically, basic machine learning (ML) and deep learning (DL) models receive input data and its matching output. Within the model, these models generate rules. In a physics-guided model, input and output rules are provided to optimize the model’s learning, hence enhancing the model’s loss optimization. The concept of the physics-guided neural network (PGNN) is becoming increasingly popular among researchers a
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Lu, XinPei, and Kostya (Ken) Ostrikov. "Guided ionization waves: The physics of repeatability." Applied Physics Reviews 5, no. 3 (2018): 031102. http://dx.doi.org/10.1063/1.5031445.

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Yang, Gangjun, Liping Xie, Ying Tan, and Zhihua Cui. "Artificial physics optimisation algorithm guided by diversity." International Journal of Computer Applications in Technology 46, no. 4 (2013): 369. http://dx.doi.org/10.1504/ijcat.2013.053428.

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Yusuf, Yusniati H. Muh. "PENERAPAN MODEL PEMBELAJARAN PROCESS ORIENTED GUIDED INQUIRY DAN MODEL PEMBELAJARAN GUIDED INQUIRY UNTUK MENINGKATKAN PENGETAHUAN PROSEDURAL FISIKA SISWA SMP NEGERI 3 AMANUBAN TENGAH." Jurnal Fisika : Fisika Sains dan Aplikasinya 4, no. 2 (2019): 100–107. http://dx.doi.org/10.35508/fisa.v4i2.1835.

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Abstrak Penelitian ini merupakan penelitian eksperimen dengan desain penelitian desain penelitian Randomized Control Group Pretest-Posttest Design, yang dilaksanakan di SMP Negeri 3 Amanuban Tengah pada bulan Februari tahun ajaran 2018/2019 . Adapun tujuan dari penelitian ini (1) Untuk mengetahui ada tidaknya perbedaan pengetahuan prosedural fisika siswa yang signifikan antara siswa yang diajar dengan menerapkan model pembelajaran Process Oriented Guided Inquiry Learning (POGIL) dengan siswa yang diajar dengan menerapkan model pembelajaran Guided Inquiry. (2) Untuk mengetahui peningkatan penge
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Dissertations / Theses on the topic "Physics-guided"

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Brandão, Eduardo. "Complexity Methods in Physics-Guided Machine Learning." Electronic Thesis or Diss., Saint-Etienne, 2023. http://www.theses.fr/2023STET0062.

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La complexité est facile à reconnaître mais difficile à définir : il existe de nombreuses mesures de complexité, chacune pertinente pour une application particulière.Dans le domaine de l'ingénierie des surfaces, l'auto-organisation entraîne la formation de motifs sur la matière par irradiation laser femtoseconde, ce qui a d'importantes applications biomédicales. Les détails de la formation des motifs ne sont pas entièrement compris. Dans des travaux menant à deux publications [1,2], grâce à un argument de complexité et un cadre d'apprentissage automatique guidé par la physique, nous montrons q
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Lynn, Hermes B. "Guided inquiry using the 5E instructional model with high school physics." Montana State University, 2012. http://etd.lib.montana.edu/etd/2012/lynn/LynnH0812.pdf.

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My project focused on the effectiveness of guided inquiry strategies to help students learn physics. Physics is a subject which lends itself to inquiry teaching, where students discover the answers to scientific questions rather than being told the answer. The study involved twelve high school physics students. Student interviews, surveys, and conceptual assessments were used to gauge the effectiveness of this teaching strategy. I collected data and compared two treatment units (inquiry) to a nontreatment unit (traditional). Results were expressed as a percent change in student learning. I als
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Shahdi, Arya. "Physics-guided Machine Learning Approaches for Applications in Geothermal Energy Prediction." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103603.

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In the area of geothermal energy mapping, scientists have used physics-based models and bottom-hole temperature measurements from oil and gas wells to generate heat flow and temperature-at-depth maps. Given the uncertainties and simplifying assumptions associated with the current state of physics-based models used in this field, this thesis explores an alternate approach for locating geothermally active regions using machine learning methods coupled with physics knowledge of geothermal energy problems, in the emerging field of physics-guided machine learning. There are two primary contribution
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Pizzati, Fabio <1993&gt. "Exploring domain-informed and physics-guided learning in image-to-image translation." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amsdottorato.unibo.it/10499/1/pizzati_fabio_tesi.pdf.

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Image-to-image (i2i) translation networks can generate fake images beneficial for many applications in augmented reality, computer graphics, and robotics. However, they require large scale datasets and high contextual understanding to be trained correctly. In this thesis, we propose strategies for solving these problems, improving performances of i2i translation networks by using domain- or physics-related priors. The thesis is divided into two parts. In Part I, we exploit human abstraction capabilities to identify existing relationships in images, thus defining domains that can be leveraged t
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Guisti, Brett M. "Comparison of Guided and Open Inquiry Instruction in a High School Physics Classroom." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2572.pdf.

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Baloyi, Vonani Michael. "Influence of guided inquiry-based laboratory activities on outcomes achieved in first-year physics." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/62910.

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In this study, the effect of ‗explicit reflective guided inquiry‘ (ERGI) laboratory practical activities on first-year physics students‘ understanding of nature of science (NOS) and academic performance is investigated. Ninety seven students participated in the study and were systematically assigned to the control group that did recipe-based practical activities and the experimental group that did ERGI laboratory practical activities. Both groups had to answer the same explicit reflective questions on an aspect of NOS at the end of each practical session. Data were collected using the VNOS For
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Karunanayaka, Prasanna Rasika. "A Dispersion Formula for Analyzing Modal Interference Among Guided and Free Electromagnetic Wave Modes and Other Phenomena in a Circular Optical Fiber." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1100844976.

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Mithani, Murad A. "The Development and Marketing of an Online Guided Study Program for the GRE Physics Exam Towards an Understanding of Future Instructional Methodologies." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1225102470.

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Zhao, Zhiling. "The Response of O(1S) and OH Emission Brightness to Gravity Wave Perturbations." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin989251411.

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Djordjevic, Milos. "Evaluation of Geometric Accuracy and Image Quality of an On-Board Imager (OBI)." Thesis, Stockholm University, Medical Radiation Physics (together with KI), 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-6967.

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<p>In this project several tests were performed to evaluate the performance of an On-Board Imager® (OBI) mounted on a clinical linear accelerator. The measurements were divided into three parts; geometric accuracy, image registration and couch shift accuracy, and image quality. A cube phantom containing a radiation opaque marker was used to study the agreement with treatment isocenter for both kV-images and cone-beam CT (CBCT) images. The long term stability was investigated by acquiring frontal and lateral kV images twice a week over a 3 month period. Stability in vertical and longitudinal ro
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Books on the topic "Physics-guided"

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Aristotle, ed. Aristotle's physics: A guided study. Rutgers University Press, 1995.

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Sachs, Joe. Aristotle's physics: A guided study. Rutgers University Press, 1995.

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Azzopardi, Francis. Accessible physics for A-level: Guided coursebook. Macmillan, 1995.

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(Eleftherios), Papantonopoulos E., ed. Physics of black holes: A guided tour. Springer, 2009.

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Celesia, Jon A. Preparation for introductory college physics: A guided student primer. Brooks/Cole Pub. Co., 1998.

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M, Goldbart Paul, ed. Mathematics for physics: A guided tour for graduate students. Cambridge University Press, 2009.

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Michael, Stone. Mathematics for physics: A guided tour for graduate students. Cambridge University Press, 2009.

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Nonlinear Guided-Wave Phenomena, Physics and Applications Topical Meeting (1989 Houston, Tex.). Nonlinear guided-wave phenomena: Physics and applications : summaries of papers presented at the Nonlinear Guided-Wave Phenomena, Physics Applications Topical Meeting, February 2-4, 1989, Houston, Texas. Optical Society of America, 1989.

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Laura, Judith. Second Enlarged Edition: From Kabbalah to Quantum Physics. Open Sea Press, 2008.

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Aliev, Yuri M. Guided-Wave-Produced Plasmas. Springer Berlin Heidelberg, 2000.

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Book chapters on the topic "Physics-guided"

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Kirisits, Christian, Kari Tanderup, Taran Paulsen Hellebust, and Robert Cormack. "Physics for Image-Guided Brachytherapy." In Gynecologic Radiation Therapy. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68958-4_12.

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Stegeman, G. I., and C. T. Seaton. "Nonlinear Guided Waves : Physics and Applications." In Trends in Quantum Electronics. Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-662-10624-2_16.

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Wang, Rui, Robin Walters, and Rose Yu. "Physics-Guided Deep Learning for Spatiotemporal Forecasting." In Knowledge-Guided Machine Learning. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-8.

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Xu, Siqi, and Shiguang Liu. "Physics-Guided Sound Synthesis for Rotating Blades." In Advances in Computer Graphics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61864-3_20.

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Salunkhe, Amol, Dwyer Deighan, Paul E. DesJardin, and Varun Chandola. "ChemTab: A Physics Guided Chemistry Modeling Framework." In Computational Science – ICCS 2022. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08751-6_6.

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Rivens, Ian, and John Civale. "Imaged-Guided Focused Ultrasound Therapy: Physics and Clinical Applications." In Image-guided Focused Ultrasound Therapy. CRC Press, 2024. http://dx.doi.org/10.1201/9780429162671-4.

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Jia, Xiaowei, Jared D. Willard, Anuj Karpatne, et al. "Physics-Guided Recurrent Neural Networks for Predicting Lake Water Temperature." In Knowledge-Guided Machine Learning. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-16.

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Marcu, Loredana, Eva Bezak, and Barry Allen. "Image guided radiotherapy: radiobiology and physics aspects of treatment." In Biomedical Physics in Radiotherapy for Cancer. Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-733-4_7.

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Daw, Arka, Anuj Karpatne, William D. Watkins, Jordan S. Read, and Vipin Kumar. "Physics-Guided Neural Networks (PGNN): An Application in Lake Temperature Modeling." In Knowledge-Guided Machine Learning. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-15.

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Istalkar, Prashant, Akshay Kadu, and Basudev Biswal. "Applications of Physics-Guided Machine Learning Architectures in Hydrology." In The Springer Series in Applied Machine Learning. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64403-0_1.

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Conference papers on the topic "Physics-guided"

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Seo, Dongjin, Soobin Um, Jong Chul Ye, and Haejun Chung. "Physics-Guided Diffusion Models for Inverse Design." In 2024 Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR). IEEE, 2024. http://dx.doi.org/10.1109/cleo-pr60912.2024.10676695.

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Zhang, Xin, Nanxi Chen, Jiyan Qiu, Pengcheng Shi, Xuesong Wu, and Wu Yuan. "Importance-Guided Sequential Training for Physics-Informed Neural Networks." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651329.

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Lynch, Sean, Jacob LaMountain, Bo Fan, et al. "Physics-guided hierarchical Neural Networks for Maxwell’s equations in metamaterials." In CLEO: Fundamental Science. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_fs.2024.fw4q.2.

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We develop a hierarchical approach to building a Physics-guided neural network (PGNN) for scalable solutions of Maxwell equations with high spatial resolution and illustrate the developed formalism on a metamaterial photonic funnel example.
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Rodrigo-Bonet, Esther, and Nikos Deligiannis. "Physics-Guided Graph Convolutional Deep Equilibrium Network for Environmental Data." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715398.

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Yu, X., W. Qin, T. Zhong, et al. "Polarization Correction using Physics-guided Foundation Model for Photon-Counting CT." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10655139.

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Mishra, Sourav, Rudrashis Majumder, and Suresh Sundaram. "PINGS: Physics Informed Networks with Guided Supermasks for Sequential PDE Solving." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651548.

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Morales Soto, Ismael, Qianyu Zhou, Farhad Imani, and Jiong Tang. "Machinery diagnosis leveraging machine learning assisted by physics-guided signal processing." In Digital Twins, AI, and NDE for Industry Applications and Energy Systems 2025, edited by Christopher Niezrecki and Saman Farhangdoust. SPIE, 2025. https://doi.org/10.1117/12.3051540.

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Stotko, David, Nils Wandel, and Reinhard Klein. "Physics-guided Shape-from-Template: Monocular Video Perception through Neural Surrogate Models." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01130.

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Zhang, Zhitian, Anjian Li, Angelica Lim, and Mo Chen. "Predicting Long-Term Human Behaviors in Discrete Representations via Physics-Guided Diffusion." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10802068.

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Su, Zhenhao, He Yu, Jie Wu, and Hangjie Li. "Hybrid Enhanced Control Architecture with Physics-Guided Constraints for Tiltrotor Hovering Stability." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11020285.

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Reports on the topic "Physics-guided"

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Pocher, Liam, John Rose, Travis Peery, and Jonathan Mace. Physics Guided Simulation of Electrostatic Discharge: Technical Report. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1845236.

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Schmitz, Tony. Physics-Guided Machine Learning (PGML) for Improved Aerostructure Manufacturing. Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2523619.

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Lin, Youzuo. Physics-guided Machine Learning: from Supervised Deep Networks to Unsupervised Lightweight Models. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1994110.

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Sun, Alexander, Bridget Scanlon, Clint Dawson, et al. Bridging Multiscale Processes in Earth System Models with Physics-Guided Hierarchical Machine Learning. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769682.

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Mock, Clara, Christopher Rinderspacher, and Brandon McWilliams. Physics-Guided Neural Network for Regularization and Learning Unbalanced Data Sets: A Priori Prediction of Melt Pool Width Variation in Directed Energy Deposition. DEVCOM Army Research Laboratory, 2023. http://dx.doi.org/10.21236/ad1196030.

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Smalley. L51984 Engine-Compressor Performance Data Normalization. Pipeline Research Council International, Inc. (PRCI), 2003. http://dx.doi.org/10.55274/r0011172.

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This report defines methods for normalizing parameters which relate to performance and integrity of compression equipment. As a basis for normalization, the project compares each parameter as recorded or derived during operation to a corresponding expected value. The report presents a method to characterize operational behavior by mapping, and by linear regression to develop a model which predicts the expected value as a function of certain independent variables. The report discusses active and passive options for mapping. The project has focused considerable attention on the choice of both de
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Keene, Thea. Throwing Up: A Self-Guided Study in Clay Bodies. Montana State University, 2022. https://doi.org/10.15788/s2022.curio1.

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Based on the demands of her upcoming thesis project, Studio Arts major Thea Keene delves into the reasoning and technicalities of her research in clay bodies. Keene explains the dilemma caused by the range of firing temperatures for different types of clay, and how it influences the decision to experiment with new ones. Her research includes the tedious process of mixing and curing the clay, and a bit on the physics of throwing on the wheel. After testing three low-fire clay mixtures, Keene ultimately decides to use an earthenware terracotta for its color and maneuverability on the wheel.
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