Статті в журналах з теми "Physics Informed Neural Network (PINN)"
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Kenzhebek, Y., T. S. Imankulov, and D. Zh Akhmed-Zaki. "PREDICTION OF OIL PRODUCTION USING PHYSICS-INFORMED NEURAL NETWORKS." BULLETIN Series of Physics & Mathematical Sciences 76, no. 4 (December 15, 2021): 45–50. http://dx.doi.org/10.51889/2021-4.1728-7901.06.
Повний текст джерелаNgo, Son Ich, and Young-Il Lim. "Solution and Parameter Identification of a Fixed-Bed Reactor Model for Catalytic CO2 Methanation Using Physics-Informed Neural Networks." Catalysts 11, no. 11 (October 28, 2021): 1304. http://dx.doi.org/10.3390/catal11111304.
Повний текст джерелаUsama, Muhammad, Rui Ma, Jason Hart, and Mikaela Wojcik. "Physics-Informed Neural Networks (PINNs)-Based Traffic State Estimation: An Application to Traffic Network." Algorithms 15, no. 12 (November 27, 2022): 447. http://dx.doi.org/10.3390/a15120447.
Повний текст джерелаTarkhov, Dmitriy, Tatiana Lazovskaya, and Galina Malykhina. "Constructing Physics-Informed Neural Networks with Architecture Based on Analytical Modification of Numerical Methods by Solving the Problem of Modelling Processes in a Chemical Reactor." Sensors 23, no. 2 (January 6, 2023): 663. http://dx.doi.org/10.3390/s23020663.
Повний текст джерелаXu, Peng-Fei, Chen-Bo Han, Hong-Xia Cheng, Chen Cheng, and Tong Ge. "A Physics-Informed Neural Network for the Prediction of Unmanned Surface Vehicle Dynamics." Journal of Marine Science and Engineering 10, no. 2 (January 24, 2022): 148. http://dx.doi.org/10.3390/jmse10020148.
Повний текст джерелаLee, Jonghwan. "Physics-Informed Neural Network for High Frequency Noise Performance in Quasi-Ballistic MOSFETs." Electronics 10, no. 18 (September 10, 2021): 2219. http://dx.doi.org/10.3390/electronics10182219.
Повний текст джерелаKim, Jungeun, Kookjin Lee, Dongeun Lee, Sheo Yon Jhin, and Noseong Park. "DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 8146–54. http://dx.doi.org/10.1609/aaai.v35i9.16992.
Повний текст джерелаHuang, Yi, Zhiyu Zhang, and Xing Zhang. "A Direct-Forcing Immersed Boundary Method for Incompressible Flows Based on Physics-Informed Neural Network." Fluids 7, no. 2 (January 25, 2022): 56. http://dx.doi.org/10.3390/fluids7020056.
Повний текст джерелаPrantikos, Konstantinos, Lefteri H. Tsoukalas, and Alexander Heifetz. "Physics-Informed Neural Network Solution of Point Kinetics Equations for a Nuclear Reactor Digital Twin." Energies 15, no. 20 (October 18, 2022): 7697. http://dx.doi.org/10.3390/en15207697.
Повний текст джерелаHassanaly, Malik, Peter J. Weddle, Kandler Smith, Subhayan De, Alireza Doostan, and Ryan King. "Physics-Informed Neural Network Modeling of Li-Ion Batteries." ECS Meeting Abstracts MA2022-02, no. 3 (October 9, 2022): 174. http://dx.doi.org/10.1149/ma2022-023174mtgabs.
Повний текст джерелаXiao, Chaohao, Xiaoqian Zhu, Fukang Yin, and Xiaoqun Cao. "Physics-informed neural network for solving coupled Korteweg-de Vries equations." Journal of Physics: Conference Series 2031, no. 1 (September 1, 2021): 012056. http://dx.doi.org/10.1088/1742-6596/2031/1/012056.
Повний текст джерелаMolnar, Joseph P., and Samuel J. Grauer. "Flow field tomography with uncertainty quantification using a Bayesian physics-informed neural network." Measurement Science and Technology 33, no. 6 (March 18, 2022): 065305. http://dx.doi.org/10.1088/1361-6501/ac5437.
Повний текст джерелаLawal, Zaharaddeen Karami, Hayati Yassin, Daphne Teck Ching Lai, and Azam Che Idris. "Physics-Informed Neural Network (PINN) Evolution and Beyond: A Systematic Literature Review and Bibliometric Analysis." Big Data and Cognitive Computing 6, no. 4 (November 21, 2022): 140. http://dx.doi.org/10.3390/bdcc6040140.
Повний текст джерелаSitte, Michael Philip, and Nguyen Anh Khoa Doan. "Velocity reconstruction in puffing pool fires with physics-informed neural networks." Physics of Fluids 34, no. 8 (August 2022): 087124. http://dx.doi.org/10.1063/5.0097496.
Повний текст джерелаVashisth, Divakar, and Tapan Mukerji. "Direct estimation of porosity from seismic data using rock- and wave-physics-informed neural networks." Leading Edge 41, no. 12 (December 2022): 840–46. http://dx.doi.org/10.1190/tle41120840.1.
Повний текст джерелаMa, Yaoyao, Xiaoyu Xu, Shuai Yan, and Zhuoxiang Ren. "A Preliminary Study on the Resolution of Electro-Thermal Multi-Physics Coupling Problem Using Physics-Informed Neural Network (PINN)." Algorithms 15, no. 2 (February 1, 2022): 53. http://dx.doi.org/10.3390/a15020053.
Повний текст джерелаZhang, Wenjuan, and Mohammed Al Kobaisi. "On the Monotonicity and Positivity of Physics-Informed Neural Networks for Highly Anisotropic Diffusion Equations." Energies 15, no. 18 (September 18, 2022): 6823. http://dx.doi.org/10.3390/en15186823.
Повний текст джерелаRafiq, Muhammad, Ghazala Rafiq, and Gyu Sang Choi. "DSFA-PINN: Deep Spectral Feature Aggregation Physics Informed Neural Network." IEEE Access 10 (2022): 22247–59. http://dx.doi.org/10.1109/access.2022.3153056.
Повний текст джерелаJi, Weiqi, Weilun Qiu, Zhiyu Shi, Shaowu Pan, and Sili Deng. "Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics." Journal of Physical Chemistry A 125, no. 36 (August 31, 2021): 8098–106. http://dx.doi.org/10.1021/acs.jpca.1c05102.
Повний текст джерелаGarcia Inda, Adan Jafet, Shao Ying Huang, Nevrez İmamoğlu, Ruian Qin, Tianyi Yang, Tiao Chen, Zilong Yuan, and Wenwei Yu. "Physics Informed Neural Networks (PINN) for Low Snr Magnetic Resonance Electrical Properties Tomography (MREPT)." Diagnostics 12, no. 11 (October 29, 2022): 2627. http://dx.doi.org/10.3390/diagnostics12112627.
Повний текст джерелаLeung, Wing Tat, Guang Lin, and Zecheng Zhang. "NH-PINN: Neural homogenization-based physics-informed neural network for multiscale problems." Journal of Computational Physics 470 (December 2022): 111539. http://dx.doi.org/10.1016/j.jcp.2022.111539.
Повний текст джерелаMaddu, Suryanarayana, Dominik Sturm, Christian L. Müller, and Ivo F. Sbalzarini. "Inverse Dirichlet weighting enables reliable training of physics informed neural networks." Machine Learning: Science and Technology 3, no. 1 (February 15, 2022): 015026. http://dx.doi.org/10.1088/2632-2153/ac3712.
Повний текст джерелаKatsikis, Dimitrios, Aliki D. Muradova, and Georgios E. Stavroulakis. "A Gentle Introduction to Physics-Informed Neural Networks, with Applications in Static Rod and Beam Problems." Journal of Advances in Applied & Computational Mathematics 9 (May 31, 2022): 103–28. http://dx.doi.org/10.15377/2409-5761.2022.09.8.
Повний текст джерелаMuradova, Aliki D., and Georgios E. Stavroulakis. "PHYSICS-INFORMED NEURAL NETWORKS FOR ELASTIC PLATE PROBLEMS WITH BENDING AND WINKLER-TYPE CONTACT EFFECTS." Journal of the Serbian Society for Computational Mechanics 15, no. 2 (December 30, 2021): 46–55. http://dx.doi.org/10.24874/jsscm.2021.15.02.05.
Повний текст джерелаVoigt, Jorrit, and Michael Moeckel. "Modelling dynamic 3D heat transfer in laser material processing based on physics informed neural networks." EPJ Web of Conferences 266 (2022): 02010. http://dx.doi.org/10.1051/epjconf/202226602010.
Повний текст джерелаAlmqvist, Andreas. "Fundamentals of Physics-Informed Neural Networks Applied to Solve the Reynolds Boundary Value Problem." Lubricants 9, no. 8 (August 19, 2021): 82. http://dx.doi.org/10.3390/lubricants9080082.
Повний текст джерелаLiu, Youqiong, Li Cai, Yaping Chen, and Bin Wang. "Physics-informed neural networks based on adaptive weighted loss functions for Hamilton-Jacobi equations." Mathematical Biosciences and Engineering 19, no. 12 (2022): 12866–96. http://dx.doi.org/10.3934/mbe.2022601.
Повний текст джерелаJadhav, Vishal, Anirudh Deodhar, Ashit Gupta, and Venkataramana Runkana. "Physics Informed Neural Network for Health Monitoring of an Air Preheater." PHM Society European Conference 7, no. 1 (June 29, 2022): 219–30. http://dx.doi.org/10.36001/phme.2022.v7i1.3343.
Повний текст джерелаLu, Yue, and Gang Mei. "A Deep Learning Approach for Predicting Two-Dimensional Soil Consolidation Using Physics-Informed Neural Networks (PINN)." Mathematics 10, no. 16 (August 16, 2022): 2949. http://dx.doi.org/10.3390/math10162949.
Повний текст джерелаHe, GaoYuan, YongXiang Zhao, and ChuLiang Yan. "MFLP-PINN: A physics-informed neural network for multiaxial fatigue life prediction." European Journal of Mechanics - A/Solids 98 (March 2023): 104889. http://dx.doi.org/10.1016/j.euromechsol.2022.104889.
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 джерелаCheng, Chen, and Guang-Tao Zhang. "Deep Learning Method Based on Physics Informed Neural Network with Resnet Block for Solving Fluid Flow Problems." Water 13, no. 4 (February 5, 2021): 423. http://dx.doi.org/10.3390/w13040423.
Повний текст джерелаDe Florio, Mario, Enrico Schiassi, and Roberto Furfaro. "Physics-informed neural networks and functional interpolation for stiff chemical kinetics." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 6 (June 2022): 063107. http://dx.doi.org/10.1063/5.0086649.
Повний текст джерелаPioch, Fabian, Jan Hauke Harmening, Andreas Maximilian Müller, Franz-Josef Peitzmann, Dieter Schramm, and Ould el Moctar. "Turbulence Modeling for Physics-Informed Neural Networks: Comparison of Different RANS Models for the Backward-Facing Step Flow." Fluids 8, no. 2 (January 26, 2023): 43. http://dx.doi.org/10.3390/fluids8020043.
Повний текст джерелаTang, Hesheng, Yangyang Liao, Hu Yang, and Liyu Xie. "A transfer learning-physics informed neural network (TL-PINN) for vortex-induced vibration." Ocean Engineering 266 (December 2022): 113101. http://dx.doi.org/10.1016/j.oceaneng.2022.113101.
Повний текст джерелаPraditia, Timothy, Thilo Walser, Sergey Oladyshkin, and Wolfgang Nowak. "Improving Thermochemical Energy Storage Dynamics Forecast with Physics-Inspired Neural Network Architecture." Energies 13, no. 15 (July 29, 2020): 3873. http://dx.doi.org/10.3390/en13153873.
Повний текст джерелаSchiassi, Enrico, Mario De Florio, Andrea D’Ambrosio, Daniele Mortari, and Roberto Furfaro. "Physics-Informed Neural Networks and Functional Interpolation for Data-Driven Parameters Discovery of Epidemiological Compartmental Models." Mathematics 9, no. 17 (August 27, 2021): 2069. http://dx.doi.org/10.3390/math9172069.
Повний текст джерелаCarpenter, Chris. "Physics-Informed Neural Networks Help Predict Fluid Flow in Porous Media." Journal of Petroleum Technology 74, no. 07 (July 1, 2022): 52–54. http://dx.doi.org/10.2118/0722-0052-jpt.
Повний текст джерелаGuo, Yanan, Xiaoqun Cao, Bainian Liu, and Mei Gao. "Solving Partial Differential Equations Using Deep Learning and Physical Constraints." Applied Sciences 10, no. 17 (August 26, 2020): 5917. http://dx.doi.org/10.3390/app10175917.
Повний текст джерелаZhang, Yabin, Haiyi Liu, Lei Wang, and Wenrong Sun. "The line rogue wave solutions of the nonlocal Davey–Stewartson I equation with PT symmetry based on the improved physics-informed neural network." Chaos: An Interdisciplinary Journal of Nonlinear Science 33, no. 1 (January 2023): 013118. http://dx.doi.org/10.1063/5.0102741.
Повний текст джерелаSantana, Vinicius V., Marlon S. Gama, Jose M. Loureiro, Alírio E. Rodrigues, Ana M. Ribeiro, Frederico W. Tavares, Amaro G. Barreto, and Idelfonso B. R. Nogueira. "A First Approach towards Adsorption-Oriented Physics-Informed Neural Networks: Monoclonal Antibody Adsorption Performance on an Ion-Exchange Column as a Case Study." ChemEngineering 6, no. 2 (March 1, 2022): 21. http://dx.doi.org/10.3390/chemengineering6020021.
Повний текст джерелаChiu, Pao-Hsiung, Jian Cheng Wong, Chinchun Ooi, My Ha Dao, and Yew-Soon Ong. "CAN-PINN: A fast physics-informed neural network based on coupled-automatic–numerical differentiation method." Computer Methods in Applied Mechanics and Engineering 395 (May 2022): 114909. http://dx.doi.org/10.1016/j.cma.2022.114909.
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 джерелаZhai, Hanfeng, Quan Zhou, and Guohui Hu. "Predicting micro-bubble dynamics with semi-physics-informed deep learning." AIP Advances 12, no. 3 (March 1, 2022): 035153. http://dx.doi.org/10.1063/5.0079602.
Повний текст джерелаBandai, Toshiyuki, and Teamrat A. Ghezzehei. "Forward and inverse modeling of water flow in unsaturated soils with discontinuous hydraulic conductivities using physics-informed neural networks with domain decomposition." Hydrology and Earth System Sciences 26, no. 16 (August 30, 2022): 4469–95. http://dx.doi.org/10.5194/hess-26-4469-2022.
Повний текст джерелаMehta, Pavan Pranjivan, Guofei Pang, Fangying Song, and George Em Karniadakis. "Discovering a universal variable-order fractional model for turbulent Couette flow using a physics-informed neural network." Fractional Calculus and Applied Analysis 22, no. 6 (December 18, 2019): 1675–88. http://dx.doi.org/10.1515/fca-2019-0086.
Повний текст джерелаZhai, Hanfeng, and Timothy Sands. "Controlling Chaos in Van Der Pol Dynamics Using Signal-Encoded Deep Learning." Mathematics 10, no. 3 (January 30, 2022): 453. http://dx.doi.org/10.3390/math10030453.
Повний текст джерелаOldenburg, Jan, Finja Borowski, Klaus-Peter Schmitz, and Michael Stiehm. "Computation of flow through TAVI device by means of physics informed neural networks." Current Directions in Biomedical Engineering 8, no. 2 (August 1, 2022): 741–44. http://dx.doi.org/10.1515/cdbme-2022-1189.
Повний текст джерелаWandel, Nils, Michael Weinmann, Michael Neidlin, and Reinhard Klein. "Spline-PINN: Approaching PDEs without Data Using Fast, Physics-Informed Hermite-Spline CNNs." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8529–38. http://dx.doi.org/10.1609/aaai.v36i8.20830.
Повний текст джерелаYang, Yuting, and Gang Mei. "A Deep Learning-Based Approach for a Numerical Investigation of Soil–Water Vertical Infiltration with Physics-Informed Neural Networks." Mathematics 10, no. 16 (August 15, 2022): 2945. http://dx.doi.org/10.3390/math10162945.
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