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Статті в журналах з теми "Physics-informed Machine Learning"
Pateras, Joseph, Pratip Rana, and Preetam Ghosh. "A Taxonomic Survey of Physics-Informed Machine Learning." Applied Sciences 13, no. 12 (June 7, 2023): 6892. http://dx.doi.org/10.3390/app13126892.
Повний текст джерелаXypakis, Emmanouil, Valeria deTurris, Fabrizio Gala, Giancarlo Ruocco, and Marco Leonetti. "Physics-informed machine learning for microscopy." EPJ Web of Conferences 266 (2022): 04007. http://dx.doi.org/10.1051/epjconf/202226604007.
Повний текст джерелаKarimpouli, Sadegh, and Pejman Tahmasebi. "Physics informed machine learning: Seismic wave equation." Geoscience Frontiers 11, no. 6 (November 2020): 1993–2001. http://dx.doi.org/10.1016/j.gsf.2020.07.007.
Повний текст джерелаBarmparis, G. D., and G. P. Tsironis. "Discovering nonlinear resonances through physics-informed machine learning." Journal of the Optical Society of America B 38, no. 9 (August 2, 2021): C120. http://dx.doi.org/10.1364/josab.430206.
Повний текст джерелаPilania, G., K. J. McClellan, C. R. Stanek, and B. P. Uberuaga. "Physics-informed machine learning for inorganic scintillator discovery." Journal of Chemical Physics 148, no. 24 (June 28, 2018): 241729. http://dx.doi.org/10.1063/1.5025819.
Повний текст джерелаLagomarsino-Oneto, Daniele, Giacomo Meanti, Nicolò Pagliana, Alessandro Verri, Andrea Mazzino, Lorenzo Rosasco, and Agnese Seminara. "Physics informed machine learning for wind speed prediction." Energy 268 (April 2023): 126628. http://dx.doi.org/10.1016/j.energy.2023.126628.
Повний текст джерелаTóth, Máté, Adam Brown, Elizabeth Cross, Timothy Rogers, and Neil D. Sims. "Resource-efficient machining through physics-informed machine learning." Procedia CIRP 117 (2023): 347–52. http://dx.doi.org/10.1016/j.procir.2023.03.059.
Повний текст джерелаLympany, Shane V., Matthew F. Calton, Mylan R. Cook, Kent L. Gee, and Mark K. Transtrum. "Mapping ambient sound levels using physics-informed machine learning." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A48—A49. http://dx.doi.org/10.1121/10.0015498.
Повний текст джерелаLee, Jonghwan. "Physics-informed machine learning model for bias temperature instability." AIP Advances 11, no. 2 (February 1, 2021): 025111. http://dx.doi.org/10.1063/5.0040100.
Повний текст джерелаMondal, B., T. Mukherjee, and T. DebRoy. "Crack free metal printing using physics informed machine learning." Acta Materialia 226 (March 2022): 117612. http://dx.doi.org/10.1016/j.actamat.2021.117612.
Повний текст джерелаДисертації з теми "Physics-informed Machine Learning"
Mack, Jonas. "Physics Informed Machine Learning of Nonlinear Partial Differential Equations." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-441275.
Повний текст джерелаWu, Jinlong. "Predictive Turbulence Modeling with Bayesian Inference and Physics-Informed Machine Learning." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/85129.
Повний текст джерелаPh. D.
Reynolds-Averaged Navier–Stokes (RANS) simulations are widely used for engineering design and analysis involving turbulent flows. In RANS simulations, the Reynolds stress needs closure models and the existing models have large model-form uncertainties. Therefore, the RANS simulations are known to be unreliable in many flows of engineering relevance, including flows with three-dimensional structures, swirl, pressure gradients, or curvature. This lack of accuracy in complex flows has diminished the utility of RANS simulations as a predictive tool for engineering design, analysis, optimization, and reliability assessments. Recently, data-driven methods have emerged as a promising alternative to develop the model of Reynolds stress for RANS simulations. In this dissertation I explore two physics-informed, data-driven frameworks to improve RANS modeled Reynolds stresses. First, a Bayesian inference framework is proposed to quantify and reduce the model-form uncertainty of RANS modeled Reynolds stress by leveraging online sparse measurement data with empirical prior knowledge. Second, a machine-learning-assisted framework is proposed to utilize offline high fidelity simulation databases. Numerical results show that the data-driven RANS models have better prediction of Reynolds stress and other quantities of interest for several canonical flows. Two metrics are also presented for an a priori assessment of the prediction confidence for the machine-learning-assisted RANS model. The proposed data-driven methods are also applicable to the computational study of other physical systems whose governing equations have some unresolved physics to be modeled.
Cedergren, Linnéa. "Physics-informed Neural Networks for Biopharma Applications." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-185423.
Повний текст джерелаWang, Jianxun. "Physics-Informed, Data-Driven Framework for Model-Form Uncertainty Estimation and Reduction in RANS Simulations." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/77035.
Повний текст джерелаPh. D.
Частини книг з теми "Physics-informed Machine Learning"
Wang, Sifan, and Paris Perdikaris. "Adaptive Training Strategies for Physics-Informed Neural Networks." In Knowledge-Guided Machine Learning, 133–60. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-6.
Повний текст джерелаCross, Elizabeth J., S. J. Gibson, M. R. Jones, D. J. Pitchforth, S. Zhang, and T. J. Rogers. "Physics-Informed Machine Learning for Structural Health Monitoring." In Structural Integrity, 347–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81716-9_17.
Повний текст джерелаSun, Alexander Y., Hongkyu Yoon, Chung-Yan Shih, and Zhi Zhong. "Applications of Physics-Informed Scientific Machine Learning in Subsurface Science: A Survey." In Knowledge-Guided Machine Learning, 111–32. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-5.
Повний текст джерелаMo, Zhaobin, Yongjie Fu, Daran Xu, and Xuan Di. "TrafficFlowGAN: Physics-Informed Flow Based Generative Adversarial Network for Uncertainty Quantification." In Machine Learning and Knowledge Discovery in Databases, 323–39. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26409-2_20.
Повний текст джерелаUhrich, Benjamin, Martin Schäfer, Oliver Theile, and Erhard Rahm. "Using Physics-Informed Machine Learning to Optimize 3D Printing Processes." In Progress in Digital and Physical Manufacturing, 206–21. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-33890-8_18.
Повний текст джерелаSankaran, Sathish, and Hardik Zalavadia. "Hybrid Data-Driven and Physics-Informed Reservoir Modeling for Unconventional Reservoirs." In Machine Learning Applications in Subsurface Energy Resource Management, 143–64. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003207009-12.
Повний текст джерелаMartín-González, Elena, Ebraham Alskaf, Amedeo Chiribiri, Pablo Casaseca-de-la-Higuera, Carlos Alberola-López, Rita G. Nunes, and Teresa Correia. "Physics-Informed Self-supervised Deep Learning Reconstruction for Accelerated First-Pass Perfusion Cardiac MRI." In Machine Learning for Medical Image Reconstruction, 86–95. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88552-6_9.
Повний текст джерелаPateras, Joseph, Ashwin Vaidya, and Preetam Ghosh. "Physics-Informed Bias Method for Multiphysics Machine Learning: Reduced Order Amyloid-β Fibril Aggregation." In Recent Advances in Mechanics and Fluid-Structure Interaction with Applications, 157–65. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14324-3_7.
Повний текст джерелаDat, Tran Tien, Yasunao Matsumoto, and Ji Dang. "A Preliminary Study on Physics-Informed Machine Learning-Based Structure Health Monitoring for Beam Structures." In Lecture Notes in Civil Engineering, 490–99. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39117-0_50.
Повний текст джерелаIbrahim, Abdul Qadir, Sebastian Götschel, and Daniel Ruprecht. "Parareal with a Physics-Informed Neural Network as Coarse Propagator." In Euro-Par 2023: Parallel Processing, 649–63. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39698-4_44.
Повний текст джерелаТези доповідей конференцій з теми "Physics-informed Machine Learning"
Manasipov, Roman, Denis Nikolaev, Dmitrii Didenko, Ramez Abdalla, and Michael Stundner. "Physics Informed Machine Learning for Production Forecast." In SPE Reservoir Characterisation and Simulation Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212666-ms.
Повний текст джерелаBaseman, Elisabeth, Nathan Debardeleben, Sean Blanchard, Juston Moore, Olena Tkachenko, Kurt Ferreira, Taniya Siddiqua, and Vilas Sridharan. "Physics-Informed Machine Learning for DRAM Error Modeling." In 2018 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT). IEEE, 2018. http://dx.doi.org/10.1109/dft.2018.8602983.
Повний текст джерелаKutz, Nathan, Diya Sashidhar, Shervin Sahba, Steven L. Brunton, Austin McDaniel, and Christopher Wilcox. "Physics-informed machine-learning for modeling aero-optics." In Applied Optical Metrology IV, edited by Erik Novak, James D. Trolinger, and Christopher C. Wilcox. SPIE, 2021. http://dx.doi.org/10.1117/12.2596540.
Повний текст джерелаKim, Junyung, Asad Ullah Shah, Hyun Kang, and Xingang Zhao. "Physics-Informed Machine Learning-Aided System Space Discretization." In 12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2021). Illinois: American Nuclear Society, 2021. http://dx.doi.org/10.13182/t124-34648.
Повний текст джерелаTetali, Harsha Vardhan, K. Supreet Alguri, and Joel B. Harley. "Wave Physics Informed Dictionary Learning In One Dimension." In 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2019. http://dx.doi.org/10.1109/mlsp.2019.8918835.
Повний текст джерелаGhosh, Abantika, Mohannad Elhamod, Jie Bu, Wei-Cheng Lee, Anuj Karpatne, and Viktor A. Podolskiy. "Physics-Informed Machine Learning of Optical Modes in Composites." In CLEO: QELS_Fundamental Science. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/cleo_qels.2022.ftu1b.1.
Повний текст джерелаWong, Benjamin, Murali Damodaran, and Boo Cheong Khoo. "Physics-Informed Machine Learning for Inverse Airfoil Shape Design." In AIAA AVIATION 2023 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2023. http://dx.doi.org/10.2514/6.2023-4374.
Повний текст джерелаLeiteritz, Raphael, Marcel Hurler, and Dirk Pfluger. "Learning Free-Surface Flow with Physics-Informed Neural Networks." In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2021. http://dx.doi.org/10.1109/icmla52953.2021.00266.
Повний текст джерелаSantos, Rogerio, Ijar DA FONSECA, and Domingos Rade. "Physics Informed Machine Learning for Path Planning of Space Robots." In XIX International Symposium on Dynamic Problems of Mechanics. ABCM, 2023. http://dx.doi.org/10.26678/abcm.diname2023.din2023-0030.
Повний текст джерелаHuber, Lilach Goren, Thomas Palmé, and Manuel Arias Chao. "Physics-Informed Machine Learning for Predictive Maintenance: Applied Use-Cases." In 2023 10th IEEE Swiss Conference on Data Science (SDS). IEEE, 2023. http://dx.doi.org/10.1109/sds57534.2023.00016.
Повний текст джерелаЗвіти організацій з теми "Physics-informed Machine Learning"
Martinez, Carianne, Jessica Jones, Drew Levin, Nathaniel Trask, and Patrick Finley. Physics-Informed Machine Learning for Epidemiological Models. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1706217.
Повний текст джерелаWang, Jianxun, Jinlong Wu, Julia Ling, Gianluca Iaccarino, and Heng Xiao. Physics-Informed Machine Learning for Predictive Turbulence Modeling: Towards a Complete Framework. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1562229.
Повний текст джерелаUllrich, Paul, Tapio Schneider, and Da Yang. Physics-Informed Machine Learning from Observations for Clouds, Convection, and Precipitation Parameterizations and Analysis. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769762.
Повний текст джерелаGhanshyam, Pilania, Kenneth James McClellan, Christopher Richard Stanek, and Blas P. Uberuaga. Physics-Informed Machine Learning for Discovery and Optimization of Materials: A Case Study of Scintillators. Office of Scientific and Technical Information (OSTI), August 2018. http://dx.doi.org/10.2172/1463529.
Повний текст джерелаBao, Jie, Chao Wang, Zhijie Xu, and Brian J. Koeppel. Physics-Informed Machine Learning with Application to Solid Oxide Fuel Cell System Modeling and Optimization. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1569289.
Повний текст джерелаFan, Jiwen, Zhangshuan Hou, Paul O'Gorman, Jessika Trancik, John Allen, Peeyush Kumar, Ranveer Chandra, Jingyu Wang, and Lai-Yung Leung. Develop a weather-aware climate model to understand and predict extremes and associated power outages and renewable energy shortageswith uncertainty-aware and physics-informed machine learning. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769695.
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