Artículos de revistas sobre el tema "Physics-informed Machine Learning"
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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 completoHowland, Michael F. y John O. Dabiri. "Wind Farm Modeling with Interpretable Physics-Informed Machine Learning". Energies 12, n.º 14 (16 de julio de 2019): 2716. http://dx.doi.org/10.3390/en12142716.
Texto completoTartakovsky, A. M., D. A. Barajas-Solano y Q. He. "Physics-informed machine learning with conditional Karhunen-Loève expansions". Journal of Computational Physics 426 (febrero de 2021): 109904. http://dx.doi.org/10.1016/j.jcp.2020.109904.
Texto completoHsu, Abigail, Baolian Cheng y Paul A. Bradley. "Analysis of NIF scaling using physics informed machine learning". Physics of Plasmas 27, n.º 1 (enero de 2020): 012703. http://dx.doi.org/10.1063/1.5130585.
Texto completoKarpov, Platon I., Chengkun Huang, Iskandar Sitdikov, Chris L. Fryer, Stan Woosley y Ghanshyam Pilania. "Physics-informed Machine Learning for Modeling Turbulence in Supernovae". Astrophysical Journal 940, n.º 1 (1 de noviembre de 2022): 26. http://dx.doi.org/10.3847/1538-4357/ac88cc.
Texto completoTetali, Harsha Vardhan y Joel Harley. "A physics-informed machine learning based dispersion curve estimation for non-homogeneous media". Journal of the Acoustical Society of America 152, n.º 4 (octubre de 2022): A239. http://dx.doi.org/10.1121/10.0016136.
Texto completoKutz, J. Nathan y Steven L. Brunton. "Parsimony as the ultimate regularizer for physics-informed machine learning". Nonlinear Dynamics 107, n.º 3 (20 de enero de 2022): 1801–17. http://dx.doi.org/10.1007/s11071-021-07118-3.
Texto completoCorson, Gregory, Jaydeep Karandikar y Tony Schmitz. "Physics-informed Bayesian machine learning case study: Integral blade rotors". Journal of Manufacturing Processes 85 (enero de 2023): 503–14. http://dx.doi.org/10.1016/j.jmapro.2022.12.004.
Texto completoMeguerdijian, Saro, Rajesh J. Pawar, Bailian Chen, Carl W. Gable, Terry A. Miller y Birendra Jha. "Physics-informed machine learning for fault-leakage reduced-order modeling". International Journal of Greenhouse Gas Control 125 (mayo de 2023): 103873. http://dx.doi.org/10.1016/j.ijggc.2023.103873.
Texto completoSharma, Pushan, Wai Tong Chung, Bassem Akoush y Matthias Ihme. "A Review of Physics-Informed Machine Learning in Fluid Mechanics". Energies 16, n.º 5 (28 de febrero de 2023): 2343. http://dx.doi.org/10.3390/en16052343.
Texto completoZeng, Shi y Dechang Pi. "Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning". Sensors 23, n.º 10 (22 de mayo de 2023): 4969. http://dx.doi.org/10.3390/s23104969.
Texto completoOmar, Sara Ibrahim, Chen Keasar, Ariel J. Ben-Sasson y Eldad Haber. "Protein Design Using Physics Informed Neural Networks". Biomolecules 13, n.º 3 (1 de marzo de 2023): 457. http://dx.doi.org/10.3390/biom13030457.
Texto completoManzoor, Tayyab, Hailong Pei, Zhongqi Sun y Zihuan Cheng. "Model Predictive Control Technique for Ducted Fan Aerial Vehicles Using Physics-Informed Machine Learning". Drones 7, n.º 1 (21 de diciembre de 2022): 4. http://dx.doi.org/10.3390/drones7010004.
Texto completoKashinath, K., M. Mustafa, A. Albert, J.-L. Wu, C. Jiang, S. Esmaeilzadeh, K. Azizzadenesheli et al. "Physics-informed machine learning: case studies for weather and climate modelling". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, n.º 2194 (15 de febrero de 2021): 20200093. http://dx.doi.org/10.1098/rsta.2020.0093.
Texto completoWenzel, Sören, Elena Slomski-Vetter y Tobias Melz. "Optimizing System Reliability in Additive Manufacturing Using Physics-Informed Machine Learning". Machines 10, n.º 7 (29 de junio de 2022): 525. http://dx.doi.org/10.3390/machines10070525.
Texto completoFang, Dehong y Jifu Tan. "Immersed boundary-physics informed machine learning approach for fluid–solid coupling". Ocean Engineering 263 (noviembre de 2022): 112360. http://dx.doi.org/10.1016/j.oceaneng.2022.112360.
Texto completoRaymond, Samuel J., David Collins y John Willams. "Designing acoustofluidic devices using a simplified physics-informed machine learning approach". Journal of the Acoustical Society of America 151, n.º 4 (abril de 2022): A254. http://dx.doi.org/10.1121/10.0011237.
Texto completoChen, Wenqian, Qian Wang, Jan S. Hesthaven y Chuhua Zhang. "Physics-informed machine learning for reduced-order modeling of nonlinear problems". Journal of Computational Physics 446 (diciembre de 2021): 110666. http://dx.doi.org/10.1016/j.jcp.2021.110666.
Texto completoMiller, Scott T., John F. Lindner, Anshul Choudhary, Sudeshna Sinha y William L. Ditto. "The scaling of physics-informed machine learning with data and dimensions". Chaos, Solitons & Fractals: X 5 (marzo de 2020): 100046. http://dx.doi.org/10.1016/j.csfx.2020.100046.
Texto completoSrinivasan, Shriram, Eric Cawi, Jeffrey Hyman, Dave Osthus, Aric Hagberg, Hari Viswanathan y Gowri Srinivasan. "Physics-informed machine learning for backbone identification in discrete fracture networks". Computational Geosciences 24, n.º 3 (17 de mayo de 2020): 1429–44. http://dx.doi.org/10.1007/s10596-020-09962-5.
Texto completoZhang, Xinlei, Jinlong Wu, Olivier Coutier-Delgosha y Heng Xiao. "Recent progress in augmenting turbulence models with physics-informed machine learning". Journal of Hydrodynamics 31, n.º 6 (diciembre de 2019): 1153–58. http://dx.doi.org/10.1007/s42241-019-0089-y.
Texto completoXie, Chiyu, Shuyi Du, Jiulong Wang, Junming Lao y Hongqing Song. "Intelligent modeling with physics-informed machine learning for petroleum engineering problems". Advances in Geo-Energy Research 8, n.º 2 (12 de marzo de 2023): 71–75. http://dx.doi.org/10.46690/ager.2023.05.01.
Texto completoTa, Hoa, Shi Wen Wong, Nathan McClanahan, Jung-Han Kimn y Kaiqun Fu. "Exploration on Physics-Informed Neural Networks on Partial Differential Equations (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 13 (26 de junio de 2023): 16344–45. http://dx.doi.org/10.1609/aaai.v37i13.27032.
Texto completoPombo, Daniel Vázquez, Peder Bacher, Charalampos Ziras, Henrik W. Bindner, Sergiu V. Spataru y Poul E. Sørensen. "Benchmarking physics-informed machine learning-based short term PV-power forecasting tools". Energy Reports 8 (noviembre de 2022): 6512–20. http://dx.doi.org/10.1016/j.egyr.2022.05.006.
Texto completoSoriano, Mario A., Helen G. Siegel, Nicholaus P. Johnson, Kristina M. Gutchess, Boya Xiong, Yunpo Li, Cassandra J. Clark, Desiree L. Plata, Nicole C. Deziel y James E. Saiers. "Assessment of groundwater well vulnerability to contamination through physics-informed machine learning". Environmental Research Letters 16, n.º 8 (22 de julio de 2021): 084013. http://dx.doi.org/10.1088/1748-9326/ac10e0.
Texto completoShih, Yueh-Ting, Yunfeng Shi y Liping Huang. "Predicting glass properties by using physics- and chemistry-informed machine learning models". Journal of Non-Crystalline Solids 584 (mayo de 2022): 121511. http://dx.doi.org/10.1016/j.jnoncrysol.2022.121511.
Texto completoLakshminarayana, Subhash, Saurav Sthapit y Carsten Maple. "Application of Physics-Informed Machine Learning Techniques for Power Grid Parameter Estimation". Sustainability 14, n.º 4 (11 de febrero de 2022): 2051. http://dx.doi.org/10.3390/su14042051.
Texto completoMudunuru, M. K. y S. Karra. "Physics-informed machine learning models for predicting the progress of reactive-mixing". Computer Methods in Applied Mechanics and Engineering 374 (febrero de 2021): 113560. http://dx.doi.org/10.1016/j.cma.2020.113560.
Texto completoShan, Liqun, Chengqian Liu, Yanchang Liu, Yazhou Tu, Linyu Deng y Xiali Hei. "Physics-informed machine learning for solving partial differential equations in porous media". Advances in Geo-Energy Research 8, n.º 1 (10 de enero de 2023): 37–44. http://dx.doi.org/10.46690/ager.2023.04.04.
Texto completoEdwards, Chris. "Neural networks learn to speed up simulations". Communications of the ACM 65, n.º 5 (abril de 2022): 27–29. http://dx.doi.org/10.1145/3524015.
Texto completoSchröder, Laura, Nikolay Krasimirov Dimitrov, David Robert Verelst y John Aasted Sørensen. "Using Transfer Learning to Build Physics-Informed Machine Learning Models for Improved Wind Farm Monitoring". Energies 15, n.º 2 (13 de enero de 2022): 558. http://dx.doi.org/10.3390/en15020558.
Texto completoArifuzzaman, S. M., Kejun Dong y Aibing Yu. "Process model of vibrating screen based on DEM and physics-informed machine learning". Powder Technology 410 (septiembre de 2022): 117869. http://dx.doi.org/10.1016/j.powtec.2022.117869.
Texto completoBorrel-Jensen, Nikolas, Allan P. Engsig-Karup y Cheol-Ho Jeong. "Machine learning-based room acoustics using flow maps and physics-informed neural networks". Journal of the Acoustical Society of America 151, n.º 4 (abril de 2022): A232—A233. http://dx.doi.org/10.1121/10.0011164.
Texto completoGuo, Shenghan, Mohit Agarwal, Clayton Cooper, Qi Tian, Robert X. Gao, Weihong Guo y Y. B. Guo. "Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm". Journal of Manufacturing Systems 62 (enero de 2022): 145–63. http://dx.doi.org/10.1016/j.jmsy.2021.11.003.
Texto completoSepe, Marzia, Antonino Graziano, Maciej Badora, Alessandro Di Stazio, Luca Bellani, Michele Compare y Enrico Zio. "A physics-informed machine learning framework for predictive maintenance applied to turbomachinery assets". Journal of the Global Power and Propulsion Society, May (25 de mayo de 2021): 1–15. http://dx.doi.org/10.33737/jgpps/134845.
Texto completoCotter, Emma, Christopher Bassett y Andone Lavery. "Classification of broadband target spectra in the mesopelagic using physics-informed machine learning". Journal of the Acoustical Society of America 149, n.º 6 (junio de 2021): 3889–901. http://dx.doi.org/10.1121/10.0005114.
Texto completoDu, Y., T. Mukherjee y T. DebRoy. "Physics-informed machine learning and mechanistic modeling of additive manufacturing to reduce defects". Applied Materials Today 24 (septiembre de 2021): 101123. http://dx.doi.org/10.1016/j.apmt.2021.101123.
Texto completoKim, Kyung Mo, Paul Hurley y Juliana Pacheco Duarte. "Physics-informed machine learning-aided framework for prediction of minimum film boiling temperature". International Journal of Heat and Mass Transfer 191 (agosto de 2022): 122839. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2022.122839.
Texto completoZhao, Ze, Michael Stuebner, Jim Lua, Nam Phan y Jinhui Yan. "Full-field temperature recovery during water quenching processes via physics-informed machine learning". Journal of Materials Processing Technology 303 (mayo de 2022): 117534. http://dx.doi.org/10.1016/j.jmatprotec.2022.117534.
Texto completoQian, Elizabeth, Boris Kramer, Benjamin Peherstorfer y Karen Willcox. "Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems". Physica D: Nonlinear Phenomena 406 (mayo de 2020): 132401. http://dx.doi.org/10.1016/j.physd.2020.132401.
Texto completoLiu, Sen, Branden B. Kappes, Behnam Amin-ahmadi, Othmane Benafan, Xiaoli Zhang y Aaron P. Stebner. "Physics-informed machine learning for composition – process – property design: Shape memory alloy demonstration". Applied Materials Today 22 (marzo de 2021): 100898. http://dx.doi.org/10.1016/j.apmt.2020.100898.
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