Literatura académica sobre el tema "Physics-informed Machine Learning"
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Artículos de revistas sobre el tema "Physics-informed Machine Learning"
Pateras, Joseph, Pratip Rana y Preetam Ghosh. "A Taxonomic Survey of Physics-Informed Machine Learning". Applied Sciences 13, n.º 12 (7 de junio de 2023): 6892. http://dx.doi.org/10.3390/app13126892.
Texto completoXypakis, Emmanouil, Valeria deTurris, Fabrizio Gala, Giancarlo Ruocco y Marco Leonetti. "Physics-informed machine learning for microscopy". EPJ Web of Conferences 266 (2022): 04007. http://dx.doi.org/10.1051/epjconf/202226604007.
Texto completoKarimpouli, Sadegh y Pejman Tahmasebi. "Physics informed machine learning: Seismic wave equation". Geoscience Frontiers 11, n.º 6 (noviembre de 2020): 1993–2001. http://dx.doi.org/10.1016/j.gsf.2020.07.007.
Texto completoBarmparis, G. D. y G. P. Tsironis. "Discovering nonlinear resonances through physics-informed machine learning". Journal of the Optical Society of America B 38, n.º 9 (2 de agosto de 2021): C120. http://dx.doi.org/10.1364/josab.430206.
Texto completoPilania, G., K. J. McClellan, C. R. Stanek y B. P. Uberuaga. "Physics-informed machine learning for inorganic scintillator discovery". Journal of Chemical Physics 148, n.º 24 (28 de junio de 2018): 241729. http://dx.doi.org/10.1063/1.5025819.
Texto completoLagomarsino-Oneto, Daniele, Giacomo Meanti, Nicolò Pagliana, Alessandro Verri, Andrea Mazzino, Lorenzo Rosasco y Agnese Seminara. "Physics informed machine learning for wind speed prediction". Energy 268 (abril de 2023): 126628. http://dx.doi.org/10.1016/j.energy.2023.126628.
Texto completoTóth, Máté, Adam Brown, Elizabeth Cross, Timothy Rogers y 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.
Texto completoLympany, Shane V., Matthew F. Calton, Mylan R. Cook, Kent L. Gee y Mark K. Transtrum. "Mapping ambient sound levels using physics-informed machine learning". Journal of the Acoustical Society of America 152, n.º 4 (octubre de 2022): A48—A49. http://dx.doi.org/10.1121/10.0015498.
Texto completoLee, Jonghwan. "Physics-informed machine learning model for bias temperature instability". AIP Advances 11, n.º 2 (1 de febrero de 2021): 025111. http://dx.doi.org/10.1063/5.0040100.
Texto completoMondal, B., T. Mukherjee y T. DebRoy. "Crack free metal printing using physics informed machine learning". Acta Materialia 226 (marzo de 2022): 117612. http://dx.doi.org/10.1016/j.actamat.2021.117612.
Texto completoTesis sobre el tema "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.
Texto completoWu, Jinlong. "Predictive Turbulence Modeling with Bayesian Inference and Physics-Informed Machine Learning". Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/85129.
Texto completoPh. 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.
Texto completoWang, 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.
Texto completoPh. D.
Capítulos de libros sobre el tema "Physics-informed Machine Learning"
Wang, Sifan y Paris Perdikaris. "Adaptive Training Strategies for Physics-Informed Neural Networks". En Knowledge-Guided Machine Learning, 133–60. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-6.
Texto completoCross, Elizabeth J., S. J. Gibson, M. R. Jones, D. J. Pitchforth, S. Zhang y T. J. Rogers. "Physics-Informed Machine Learning for Structural Health Monitoring". En Structural Integrity, 347–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81716-9_17.
Texto completoSun, Alexander Y., Hongkyu Yoon, Chung-Yan Shih y Zhi Zhong. "Applications of Physics-Informed Scientific Machine Learning in Subsurface Science: A Survey". En Knowledge-Guided Machine Learning, 111–32. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-5.
Texto completoMo, Zhaobin, Yongjie Fu, Daran Xu y Xuan Di. "TrafficFlowGAN: Physics-Informed Flow Based Generative Adversarial Network for Uncertainty Quantification". En 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.
Texto completoUhrich, Benjamin, Martin Schäfer, Oliver Theile y Erhard Rahm. "Using Physics-Informed Machine Learning to Optimize 3D Printing Processes". En 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.
Texto completoSankaran, Sathish y Hardik Zalavadia. "Hybrid Data-Driven and Physics-Informed Reservoir Modeling for Unconventional Reservoirs". En Machine Learning Applications in Subsurface Energy Resource Management, 143–64. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003207009-12.
Texto completoMartín-González, Elena, Ebraham Alskaf, Amedeo Chiribiri, Pablo Casaseca-de-la-Higuera, Carlos Alberola-López, Rita G. Nunes y Teresa Correia. "Physics-Informed Self-supervised Deep Learning Reconstruction for Accelerated First-Pass Perfusion Cardiac MRI". En 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.
Texto completoPateras, Joseph, Ashwin Vaidya y Preetam Ghosh. "Physics-Informed Bias Method for Multiphysics Machine Learning: Reduced Order Amyloid-β Fibril Aggregation". En 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.
Texto completoDat, Tran Tien, Yasunao Matsumoto y Ji Dang. "A Preliminary Study on Physics-Informed Machine Learning-Based Structure Health Monitoring for Beam Structures". En Lecture Notes in Civil Engineering, 490–99. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39117-0_50.
Texto completoIbrahim, Abdul Qadir, Sebastian Götschel y Daniel Ruprecht. "Parareal with a Physics-Informed Neural Network as Coarse Propagator". En Euro-Par 2023: Parallel Processing, 649–63. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39698-4_44.
Texto completoActas de conferencias sobre el tema "Physics-informed Machine Learning"
Manasipov, Roman, Denis Nikolaev, Dmitrii Didenko, Ramez Abdalla y Michael Stundner. "Physics Informed Machine Learning for Production Forecast". En SPE Reservoir Characterisation and Simulation Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212666-ms.
Texto completoBaseman, Elisabeth, Nathan Debardeleben, Sean Blanchard, Juston Moore, Olena Tkachenko, Kurt Ferreira, Taniya Siddiqua y Vilas Sridharan. "Physics-Informed Machine Learning for DRAM Error Modeling". En 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.
Texto completoKutz, Nathan, Diya Sashidhar, Shervin Sahba, Steven L. Brunton, Austin McDaniel y Christopher Wilcox. "Physics-informed machine-learning for modeling aero-optics". En Applied Optical Metrology IV, editado por Erik Novak, James D. Trolinger y Christopher C. Wilcox. SPIE, 2021. http://dx.doi.org/10.1117/12.2596540.
Texto completoKim, Junyung, Asad Ullah Shah, Hyun Kang y Xingang Zhao. "Physics-Informed Machine Learning-Aided System Space Discretization". En 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.
Texto completoTetali, Harsha Vardhan, K. Supreet Alguri y Joel B. Harley. "Wave Physics Informed Dictionary Learning In One Dimension". En 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2019. http://dx.doi.org/10.1109/mlsp.2019.8918835.
Texto completoGhosh, Abantika, Mohannad Elhamod, Jie Bu, Wei-Cheng Lee, Anuj Karpatne y Viktor A. Podolskiy. "Physics-Informed Machine Learning of Optical Modes in Composites". En CLEO: QELS_Fundamental Science. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/cleo_qels.2022.ftu1b.1.
Texto completoWong, Benjamin, Murali Damodaran y Boo Cheong Khoo. "Physics-Informed Machine Learning for Inverse Airfoil Shape Design". En AIAA AVIATION 2023 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2023. http://dx.doi.org/10.2514/6.2023-4374.
Texto completoLeiteritz, Raphael, Marcel Hurler y Dirk Pfluger. "Learning Free-Surface Flow with Physics-Informed Neural Networks". En 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2021. http://dx.doi.org/10.1109/icmla52953.2021.00266.
Texto completoSantos, Rogerio, Ijar DA FONSECA y Domingos Rade. "Physics Informed Machine Learning for Path Planning of Space Robots". En XIX International Symposium on Dynamic Problems of Mechanics. ABCM, 2023. http://dx.doi.org/10.26678/abcm.diname2023.din2023-0030.
Texto completoHuber, Lilach Goren, Thomas Palmé y Manuel Arias Chao. "Physics-Informed Machine Learning for Predictive Maintenance: Applied Use-Cases". En 2023 10th IEEE Swiss Conference on Data Science (SDS). IEEE, 2023. http://dx.doi.org/10.1109/sds57534.2023.00016.
Texto completoInformes sobre el tema "Physics-informed Machine Learning"
Martinez, Carianne, Jessica Jones, Drew Levin, Nathaniel Trask y Patrick Finley. Physics-Informed Machine Learning for Epidemiological Models. Office of Scientific and Technical Information (OSTI), octubre de 2020. http://dx.doi.org/10.2172/1706217.
Texto completoWang, Jianxun, Jinlong Wu, Julia Ling, Gianluca Iaccarino y Heng Xiao. Physics-Informed Machine Learning for Predictive Turbulence Modeling: Towards a Complete Framework. Office of Scientific and Technical Information (OSTI), septiembre de 2016. http://dx.doi.org/10.2172/1562229.
Texto completoUllrich, Paul, Tapio Schneider y Da Yang. Physics-Informed Machine Learning from Observations for Clouds, Convection, and Precipitation Parameterizations and Analysis. Office of Scientific and Technical Information (OSTI), abril de 2021. http://dx.doi.org/10.2172/1769762.
Texto completoGhanshyam, Pilania, Kenneth James McClellan, Christopher Richard Stanek y 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), agosto de 2018. http://dx.doi.org/10.2172/1463529.
Texto completoBao, Jie, Chao Wang, Zhijie Xu y 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), septiembre de 2019. http://dx.doi.org/10.2172/1569289.
Texto completoFan, Jiwen, Zhangshuan Hou, Paul O'Gorman, Jessika Trancik, John Allen, Peeyush Kumar, Ranveer Chandra, Jingyu Wang y 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), abril de 2021. http://dx.doi.org/10.2172/1769695.
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