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