Auswahl der wissenschaftlichen Literatur zum Thema „Physics-guided“
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Zeitschriftenartikel zum Thema "Physics-guided"
Gutierrez, Mario A., Jack Dvorkin und Amos Nur. „Stratigraphy-guided rock physics“. Leading Edge 21, Nr. 1 (Januar 2002): 98–103. http://dx.doi.org/10.1190/1.1445859.
Der volle Inhalt der QuelleEaton, D. J. „Physics Support of Image-guided Radiotherapy“. Clinical Oncology 25, Nr. 3 (März 2013): e31. http://dx.doi.org/10.1016/j.clon.2012.12.003.
Der volle Inhalt der QuellePawar, Suraj, Omer San, Burak Aksoylu, Adil Rasheed und Trond Kvamsdal. „Physics guided machine learning using simplified theories“. Physics of Fluids 33, Nr. 1 (01.01.2021): 011701. http://dx.doi.org/10.1063/5.0038929.
Der volle Inhalt der QuellePawar, Suraj, Omer San, Burak Aksoylu, Adil Rasheed und Trond Kvamsdal. „Physics guided machine learning using simplified theories“. Physics of Fluids 33, Nr. 1 (01.01.2021): 011701. http://dx.doi.org/10.1063/5.0038929.
Der volle Inhalt der QuelleKeyser, Paul T. „Aristotle's Physics: A Guided Study. Joe Sachs“. Isis 87, Nr. 4 (Dezember 1996): 716–17. http://dx.doi.org/10.1086/357668.
Der volle Inhalt der QuelleLu, XinPei, und Kostya (Ken) Ostrikov. „Guided ionization waves: The physics of repeatability“. Applied Physics Reviews 5, Nr. 3 (September 2018): 031102. http://dx.doi.org/10.1063/1.5031445.
Der volle Inhalt der QuelleYang, Gangjun, Liping Xie, Ying Tan und Zhihua Cui. „Artificial physics optimisation algorithm guided by diversity“. International Journal of Computer Applications in Technology 46, Nr. 4 (2013): 369. http://dx.doi.org/10.1504/ijcat.2013.053428.
Der volle Inhalt der QuelleYusuf, 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, Nr. 2 (25.10.2019): 100–107. http://dx.doi.org/10.35508/fisa.v4i2.1835.
Der volle Inhalt der QuelleSyamsidar, S., Khaeruddin Khaeruddin und Helmi Helmi. „The Effectiveness of using Student Worksheets to Practice Science Process Skills on Hooke's Law Material“. Jurnal Penelitian & Pengembangan Pendidikan Fisika 7, Nr. 1 (30.07.2021): 83–90. http://dx.doi.org/10.21009/1.07109.
Der volle Inhalt der QuelleMurphy, Cara P., und John P. Kerekes. „Physics-guided neural network for predicting chemical signatures“. Applied Optics 60, Nr. 11 (07.04.2021): 3176. http://dx.doi.org/10.1364/ao.420688.
Der volle Inhalt der QuelleDissertationen zum Thema "Physics-guided"
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.
Der volle Inhalt der QuelleShahdi, Arya. „Physics-guided Machine Learning Approaches for Applications in Geothermal Energy Prediction“. Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103603.
Der volle Inhalt der QuelleMaster of Science
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.
Pizzati, Fabio <1993>. „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.
Der volle Inhalt der QuelleGuisti, 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.
Der volle Inhalt der QuelleBaloyi, 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.
Der volle Inhalt der QuelleThesis (PhD)--University of Pretoria, 2017.
Science, Mathematics and Technology Education
PhD
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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.
Der volle Inhalt der QuelleMithani, 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.
Der volle Inhalt der QuelleZhao, 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.
Der volle Inhalt der QuelleDjordjevic, 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.
Der volle Inhalt der QuelleIn 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.
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.
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.
Madonia, Rosalia [Verfasser], Dr Kraus Ute [Akademischer Betreuer] Prof und 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.
Der volle Inhalt der QuelleBücher zum Thema "Physics-guided"
Aristotle, Hrsg. Aristotle's physics: A guided study. New Brunswick, N.J: Rutgers University Press, 1995.
Den vollen Inhalt der Quelle findenSachs, Joe. Aristotle's physics: A guided study. New Brunswick, N.J: Rutgers University Press, 1995.
Den vollen Inhalt der Quelle finden(Eleftherios), Papantonopoulos E., Hrsg. Physics of black holes: A guided tour. Berlin: Springer, 2009.
Den vollen Inhalt der Quelle findenAzzopardi, Francis. Accessible physics for A-level: Guided coursebook. Basingstoke: Macmillan, 1995.
Den vollen Inhalt der Quelle findenCelesia, Jon A. Preparation for introductory college physics: A guided student primer. Pacific Grove, CA: Brooks/Cole Pub. Co., 1998.
Den vollen Inhalt der Quelle findenMichael, Stone. Mathematics for physics: A guided tour for graduate students. Cambridge, UK: Cambridge University Press, 2009.
Den vollen Inhalt der Quelle findenM, Goldbart Paul, Hrsg. Mathematics for physics: A guided tour for graduate students. Cambridge, UK: Cambridge University Press, 2009.
Den vollen Inhalt der Quelle findenNonlinear 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. Washington, D.C: Optical Society of America, 1989.
Den vollen Inhalt der Quelle findenAliev, Yuri M. Guided-Wave-Produced Plasmas. Berlin: Springer Berlin Heidelberg, 2000.
Den vollen Inhalt der Quelle findenLaura, Judith. Second Enlarged Edition: From Kabbalah to Quantum Physics. USA: Open Sea Press, 2008.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Physics-guided"
Kirisits, Christian, Kari Tanderup, Taran Paulsen Hellebust und Robert Cormack. „Physics for Image-Guided Brachytherapy“. In Gynecologic Radiation Therapy, 143–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68958-4_12.
Der volle Inhalt der QuelleStegeman, G. I., und C. T. Seaton. „Nonlinear Guided Waves : Physics and Applications“. In Trends in Quantum Electronics, 179–205. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-662-10624-2_16.
Der volle Inhalt der QuelleWang, Rui, Robin Walters und Rose Yu. „Physics-Guided Deep Learning for Spatiotemporal Forecasting“. In Knowledge-Guided Machine Learning, 179–210. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-8.
Der volle Inhalt der QuelleXu, Siqi, und Shiguang Liu. „Physics-Guided Sound Synthesis for Rotating Blades“. In Advances in Computer Graphics, 233–44. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61864-3_20.
Der volle Inhalt der QuelleSalunkhe, Amol, Dwyer Deighan, Paul E. DesJardin und Varun Chandola. „ChemTab: A Physics Guided Chemistry Modeling Framework“. In Computational Science – ICCS 2022, 75–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08751-6_6.
Der volle Inhalt der QuelleJia, Xiaowei, Jared D. Willard, Anuj Karpatne, Jordan S. Read, Jacob A. Zwart, Michael Steinbach und Vipin Kumar. „Physics-Guided Recurrent Neural Networks for Predicting Lake Water Temperature“. In Knowledge-Guided Machine Learning, 373–98. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-16.
Der volle Inhalt der QuelleMarcu, Loredana, Eva Bezak und Barry Allen. „Image guided radiotherapy: radiobiology and physics aspects of treatment“. In Biomedical Physics in Radiotherapy for Cancer, 155–81. London: Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-733-4_7.
Der volle Inhalt der QuelleDaw, Arka, Anuj Karpatne, William D. Watkins, Jordan S. Read und Vipin Kumar. „Physics-Guided Neural Networks (PGNN): An Application in Lake Temperature Modeling“. In Knowledge-Guided Machine Learning, 353–72. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-15.
Der volle Inhalt der QuelleSzydlowski, Michal J., Christoph Schwingshackl und Ludovic Renson. „Modeling Nonlinear Structures Using Physics-Guided, Machine-Learnt Models“. In Nonlinear Structures & Systems, Volume 1, 71–74. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36999-5_9.
Der volle Inhalt der QuelleSemage, Buddhika Laknath, Thommen George Karimpanal, Santu Rana und 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, 674–86. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37742-6_52.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Physics-guided"
Hendrick*, Natasha, und 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.
Der volle Inhalt der QuelleLi, Yan, Shashi Shekhar, Pengyue Wang und William Northrop. „Physics-guided energy-efficient path selection“. In SIGSPATIAL '18: 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3274895.3274933.
Der volle Inhalt der QuelleLe, Huy, Anshuman Pradhan, Nader C. Dutta, Biondo Biondi, Tapan Mukerji und 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.
Der volle Inhalt der QuelleOzdagli, Ali, Peter Volgyesi und Xenofon Koutsoukos. „Surrogate Modeling using Physics-guided Learning“. In CPS-IoT Week '23: Cyber-Physical Systems and Internet of Things Week 2023. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3576914.3587532.
Der volle Inhalt der QuelleLubbe, R., und M. El Mardi. „Rock Physics Guided Quantitative Seismic Inversion“. In Third EAGE Workshop on Rock Physics. Netherlands: EAGE Publications BV, 2015. http://dx.doi.org/10.3997/2214-4609.201414388.
Der volle Inhalt der QuelleTETALI, HARSHA VARDHAN, und 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.
Der volle Inhalt der QuelleGhosh, Abantika, Mohannad Elhamod, Jie Bu, Wei-Cheng Lee, Anuj Karpatne und Viktor A. Podolskiy. „Physics-guided machine learning for Maxwell's equations“. In Metamaterials, Metadevices, and Metasystems 2021, herausgegeben von Nader Engheta, Mikhail A. Noginov und Nikolay I. Zheludev. SPIE, 2021. http://dx.doi.org/10.1117/12.2594575.
Der volle Inhalt der QuellePhan, Son, und 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.
Der volle Inhalt der QuelleYuan, Ye, Jiaming Song, Umar Iqbal, Arash Vahdat und 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.
Der volle Inhalt der QuelleNestmeyer, Thomas, Jean-Francois Lalonde, Iain Matthews und 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.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Physics-guided"
Pocher, Liam, John Rose, Travis Peery und Jonathan Mace. Physics Guided Simulation of Electrostatic Discharge: Technical Report. Office of Scientific and Technical Information (OSTI), Februar 2022. http://dx.doi.org/10.2172/1845236.
Der volle Inhalt der QuelleLin, Youzuo. Physics-guided Machine Learning: from Supervised Deep Networks to Unsupervised Lightweight Models. Office of Scientific and Technical Information (OSTI), August 2023. http://dx.doi.org/10.2172/1994110.
Der volle Inhalt der QuelleSun, Alexander, Bridget Scanlon, Clint Dawson, Paola Passalacqua, Dev Niyogi, Zong-Liang Yang und Susanne Pierce. Bridging Multiscale Processes in Earth System Models with Physics-Guided Hierarchical Machine Learning. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769682.
Der volle Inhalt der QuelleSmalley. L51984 Engine-Compressor Performance Data Normalization. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), Januar 2003. http://dx.doi.org/10.55274/r0011172.
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