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

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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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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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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 pengetahuan prosedural fisika siswa yang diajar dengan menerapkan model pembelajaran Process Oriented Guided Inquiry Learning (POGIL) lebih tinggi dibandingkan dengan siswa yang diajar dengan menerapkan model pembelajaran Guide Inquiry. Populasi dalam penelitian ini adalah seluruh siswa kelas VIII SMP Negeri 3 Amanuban Tengah. Hasil penelitian diperoleh bahwa rata-rata skor pengetahuan prosedural pada kelas eksperimen = 77,12, dan pada kelas kontrol 71,02. Dari hasil analisis menggunakan uji-t pada taraf signifikansi (α) = 0,05, diperoleh: 1) terdapat perbedaan pengetahuan prosedural antara siswa yang diajar dengan menerapkan model pembelajaran Proces Oriented Guided Inquiry Learning dengan siswa yang diajar dengan model pembelajaran Guided Inquiry dimana nilai ttable = -2,014 <thitung = 4,013> ttable = 2,014. 2) pengetahuan prosedural fisika siswa yang diajar dengan menerapkan model pembelajaran Proces Oriented Guided Inquiry Learning lebih tinggi dibandingkan dengan siswa yang diajar dengan menerapkan model pembelajaran Guided Inquiry dimana nilai thitung = 7,058> ttabel= 2,014. Kata kunci: model pembelajaran Process Oriented Guided Inquiry Learning (POGIL), Guided Inquiry, Pengetahuan Prosedural Fisika . Abstract
 [Implementation of a Learning Model for the Process Oriented Guided Inquiry and Guided Inquiry Model to Improve the Physics Procedural Knowledge of Students at SMP Negeri 3 Amanuban Tengah] This research is experiment research with Randomised control group preteest-possttesst desigen exetut at SMP Negeri 3 Amanuban Tengah on school year Februari 2018/2019. Aim of this researchhare (1) to knows an differences improve student physics procedural knowledge between students by applying process oriented guided inquiry learning model with students by applying guided inquiry model (2) to know improve students physics procedural knowledge was taught by apllying process orinted guided inquiry learning model higher than students was taught by apllying guided inquiry model. Population in this research is all of students of grade VIII SMP Negeri 3 Amanuban Tengah. Research result, it is found the average score of studentsphysics procedural knowledge in experimental class = 77,12, and control class 71,02. The result of the analisis using t-test with signifificant level α = 0,05 concluded that (1) there are diferenrence of physics procedural of student physics which is significant between students taught by applying process oriented guided inquiry learning with students taught by knowledge that is taught by applying process oriented guided inquiry learning model is higher than the students taught by applying the guided inquiry model where tcount = 7,058> ttable = 2,014.
 Keywords: Process Oriented Guided Inquiry Learning (POGIL) model, guided inquiry, physics procedural knowledge
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Syamsidar, S., Khaeruddin Khaeruddin, and Helmi Helmi. "The Effectiveness of using Student Worksheets to Practice Science Process Skills on Hooke's Law Material." Jurnal Penelitian & Pengembangan Pendidikan Fisika 7, no. 1 (2021): 83–90. http://dx.doi.org/10.21009/1.07109.

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This study aims to analyze the effectiveness of guided inquiry-based physics worksheets in training students' science process skills. This case study was conducted at SMAN 4 Sidenreng Rappang with 28 respondents. Students use worksheets based on guided inquiry for four meetings in Hooke's law material. The worksheets were developed using the ADDIE model, consisting of analysis, design, development, implementation, and evaluation, including expert validation. We obtained the expert analysis of student worksheets based on guided inquiry from two experts, namely 3.16 out of 4 Likert scales with a validity percentage of 78.89%. These results indicate that the student worksheets based on guided inquiry are appropriate. Also, the gain test is used to determine the effectiveness of this worksheet. The science process skills test score obtained an average N-gain of 0.69, and this value is included in the medium category. This is shown based on student responses which found that 28 respondents generally gave a positive reaction to guided inquiry-based physics worksheets. This means that students receive guided inquiry-based physics worksheets as an effective learning resource used in learning.
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Murphy, Cara P., and John P. Kerekes. "Physics-guided neural network for predicting chemical signatures." Applied Optics 60, no. 11 (2021): 3176. http://dx.doi.org/10.1364/ao.420688.

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Dissertations / Theses on the topic "Physics-guided"

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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 also investigated the effect of inquiry teaching on my feelings about teaching, student motivation, and student engagement. The results of my project indicate that students learn effectively through both inquiry and through traditional teaching. Student understanding results did not differ dramatically between comparison units. However, inquiry may be more engaging for students than traditional lecture style teaching. Some students experienced a decrease in motivation while others students experienced an increase in motivation, possibly indicating the existence of multiple intelligences and different learning styles or a lack of experience with the rigors of inquiry. My enjoyment of teaching increased when I taught through guided inquiry. I enjoyed seeing the students discover on their own what I was trying to teach them. I also believe that the inquiry style will better prepare students for standardized tests which emphasize process skills versus content knowledge.
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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 contributions of this thesis. First, we present a thorough analysis of using state-of-the-art machine learning models to predict a subsurface geothermal parameter, temperature-at-depth, using a rich geo-spatial dataset across the Appalachian Basin. Specifically, we explore a suite of machine learning algorithms such as neural networks (DNN), Ridge regression (R-reg) models, and decision-tree-based models (e.g., XGBoost and Random Forest). We found that XGBoost and Random Forests result in the highest accuracy for subsurface temperature prediction. We also ran our model on a fine spatial grid to provide 2D continuous temperature maps at three different depths using the XGBoost model, which can be used to locate prospective geothermally active regions. Second, we develop a physics-guided machine learning model for predicting subsurface temperatures that not only uses surface temperature, thermal conductivity coefficient, and depth as input parameters, but also the heat-flux parameter that is known to be a potent indicator of temperature-at-depth values according to physics knowledge of geothermal energy problems. Since, there is no independent easy-to-use method for observing heat-flux directly or inferring it from other observed variables. We develop an innovative approach to take into account heat-flux parameters through a physics-guided clustering-regression model. Specifically, the bottom-hole temperature data is initially clustered into multiple groups based on the heat-flux parameter using Gaussian mixture model (GMM). This is followed by training neural network regression models using the data within each constant heat-flux region. Finally, a KNN classifier is trained for cluster membership prediction. Our preliminary results indicate that our proposed approach results in lower errors as the number of clusters increases because the heat-flux parameter is indirectly accounted for in the machine learning model.<br>Master of Science<br>Machine learning and artificial intelligence have transformed many research fields and industries. In this thesis, we investigate the applicability of machine learning and data-driven approaches in the field of geothermal energy exploration. Given the uncertainties and simplifying assumptions associated with the current state of physics-based models, we show that machine learning can provide viable alternative solutions for geothermal energy mapping. First, we explore a suite of machine learning algorithms such as neural networks (DNN), Ridge regression (R-reg) models, and decision-tree based models (e.g., XGBoost and Random Forest). We find that XGBoost and Random Forests result in the highest accuracy for subsurface temperature prediction. Accuracy measures show that machine learning models are at par with physics-based models and can even outperform the thermal conductivity model. Second, we incorporate the thermal conductivity theory with machine learning and propose an innovative clustering-regression approach in the emerging area of physics-guided machine learning that results in a smaller error than black-box machine learning methods.
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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 to improve data usage efficiency. We use additional domain-related information to train networks on web-crawled data, hallucinate scenarios unseen during training, and perform few-shot learning. In Part II, we instead rely on physics priors. First, we combine realistic physics-based rendering with generative networks to boost outputs realism and controllability. Then, we exploit naive physical guidance to drive a manifold reorganization, which allowed generating continuous conditions such as timelapses.
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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 Form-C questionnaire, focus group interviews, explicit reflective questions, combined practical and theoretical year-end examinations. Using blind scoring, students‘ views were classified as informed, mixed or naïve for each aspect of NOS. The percentage of informed views was larger for the experimental group in each of the seven NOS aspects. Overall, the percentage informed views in the experimental group was larger by a statistically significant margin of 10 percentage points (p = 0.008). The largest differences were observed in the tentative nature of science, the distinction between theory and law, and the role of imagination and creativity. Additionally, males showed more informed NOS understandings than females, while low achieving students were better informed than high achievers, but the differences were not statistically significant. The experimental group did not perform any better than the control group in the practical and theoretical year-end examinations. Therefore, this study demonstrated that ERGI laboratory practical activities activities enhanced first-year physics students‘ understanding of NOS but not their academic performance.<br>Thesis (PhD)--University of Pretoria, 2017.<br>Science, Mathematics and Technology Education<br>PhD<br>Unrestricted
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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 robotic arm motion as well as the stability of the center-of-rotation was evaluated. Further, the agreement of kV image and CBCT center with MV image center was examined.</p><p>A marker seed phantom was used to evaluate and compare the three applications in image registration; 2D/2D, 2D/3D and 3D/3D. Image registration using kV-kV image sets were compared with MV MV and MV-kV image sets. Further, the accuracy in 2D/2D matches with images acquired at non-orthogonal gantry angles was evaluated. The image quality in CBCT images was evaluated using a Catphan® phantom. Hounsfield unit (HU) uniformity and linearity was compared with planning CT. HU accuracy is crucial for dose verification using CBCT data.</p><p>The geometric measurements showed good long term stability and accurate position reproducibility after robotic arm motions. A systematic error of about 1 mm in lateral direction of the kV-image center was detected. A small difference between kV and CBCT center was observed and related to a lateral kV detector offset. The vector disagreement between kV- and MV-image centers was  2 mm at some gantry angles. Image registration with the different match applications worked sufficiently. 2D/3D match was seen to correct more accurately than 2D/2D match for large translational and rotational shifts. CBCT images acquired with full-fan mode showed good HU uniformity but half fan images were less uniform. In the soft tissue region the HU agreement with planning CT was reasonable while a larger disagreement was observed at higher densities. This work shows that the OBI is robust and stable in its performance. With regular QC and calibrations the geometric precision of the OBI can be maintained within 1 mm of treatment isocenter.</p>
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Madonia, Rosalia [Verfasser], Dr Kraus Ute [Akademischer Betreuer] Prof, and Dr Grabmayr Peter [Gutachter] Prof. "A Nature of Science guided approach to the physics teaching of Cosmic Rays / Rosalia Madonia ; Gutachter: Peter Prof. Dr. Grabmayr ; Betreuer: Ute Prof. Dr. Kraus." Hildesheim : Universität Hildesheim, 2019. http://d-nb.info/1202921086/34.

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

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

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

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

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

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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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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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Szydlowski, Michal J., Christoph Schwingshackl, and Ludovic Renson. "Modeling Nonlinear Structures Using Physics-Guided, Machine-Learnt Models." In Nonlinear Structures & Systems, Volume 1. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36999-5_9.

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Semage, Buddhika Laknath, Thommen George Karimpanal, Santu Rana, and Svetha Venkatesh. "Intuitive Physics Guided Exploration for Sample Efficient Sim2real Transfer." In Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37742-6_52.

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Conference papers on the topic "Physics-guided"

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Hendrick*, Natasha, and Caitlin Davis. "Rock-Physics Guided Seismic Exploration." In International Conference and Exhibition, Melbourne, Australia 13-16 September 2015. Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2015. http://dx.doi.org/10.1190/ice2015-2211521.

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Li, Yan, Shashi Shekhar, Pengyue Wang, and William Northrop. "Physics-guided energy-efficient path selection." In SIGSPATIAL '18: 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2018. http://dx.doi.org/10.1145/3274895.3274933.

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Le, Huy, Anshuman Pradhan, Nader C. Dutta, Biondo Biondi, Tapan Mukerji, and Stewart A. Levin. "Rock physics guided velocity model building." In SEG Technical Program Expanded Abstracts 2018. Society of Exploration Geophysicists, 2018. http://dx.doi.org/10.1190/segam2018-2998543.1.

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Ozdagli, Ali, Peter Volgyesi, and Xenofon Koutsoukos. "Surrogate Modeling using Physics-guided Learning." In CPS-IoT Week '23: Cyber-Physical Systems and Internet of Things Week 2023. ACM, 2023. http://dx.doi.org/10.1145/3576914.3587532.

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Lubbe, R., and M. El Mardi. "Rock Physics Guided Quantitative Seismic Inversion." In Third EAGE Workshop on Rock Physics. EAGE Publications BV, 2015. http://dx.doi.org/10.3997/2214-4609.201414388.

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TETALI, HARSHA VARDHAN, and JOEL B. HARLEY. "PHYSICS-INFORMED GUIDED WAVEFIELD DATA COMPLETION." In Structural Health Monitoring 2023. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/shm2023/36993.

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Ultrasonic wavefields are widely employed in nondestructive testing and structural health monitoring to detect and evaluate structural damage. However, measuring wavefields continuously throughout space poses challenges and can be costly. To address this, we propose a novel approach that combines the wave equation with computer vision algorithms to visualize wavefields. Our algorithm incorporates the wave equation, which encapsulates our knowledge of wave propagation, to infer the wavefields in regions where direct measurement is not feasible. Specifically, we focus on reconstructing wavefields from partial measurements, where the wavefield data from large continuous regions are missing. The algorithm is tested on experimental data demonstrating its effectiveness in reconstructing the wavefields at unmeasured regions. This also benefits in reducing the need for expensive equipment and enhancing the accuracy of structural health monitoring at a lower cost. The results highlight the potential of our approach to advance ultrasonic wavefield imaging capabilities and open new avenues for Nondestructive testing and structural health monitoring.
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Ghosh, Abantika, Mohannad Elhamod, Jie Bu, Wei-Cheng Lee, Anuj Karpatne, and Viktor A. Podolskiy. "Physics-guided machine learning for Maxwell's equations." In Metamaterials, Metadevices, and Metasystems 2021, edited by Nader Engheta, Mikhail A. Noginov, and Nikolay I. Zheludev. SPIE, 2021. http://dx.doi.org/10.1117/12.2594575.

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Phan, Son, and Mrinal K. Sen. "Seismic nonstationary deconvolution with physics-guided autoencoder." In First International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists, 2021. http://dx.doi.org/10.1190/segam2021-3582130.1.

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Yuan, Ye, Jiaming Song, Umar Iqbal, Arash Vahdat, and Jan Kautz. "PhysDiff: Physics-Guided Human Motion Diffusion Model." In 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2023. http://dx.doi.org/10.1109/iccv51070.2023.01467.

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Nestmeyer, Thomas, Jean-Francois Lalonde, Iain Matthews, and Andreas Lehrmann. "Learning Physics-Guided Face Relighting Under Directional Light." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00517.

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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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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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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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Abstract:
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 dependent and independent variable(s) for the mapping process (guided both by knowledge of the physics involved, and by typical data from various sources for most of the parameters). The report presents a basis for assessing the statistical significance of a deviation between measured and expected values and, where appropriate, for evaluating the economic significance. In addition, the report gives trouble-shooting guidelines for use by operators in the event a significant deviation between a measured and expected parameter is identified.
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