Artykuły w czasopismach na temat „Physics-Informed neural network”
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Hofmann, Tobias, Jacob Hamar, Marcel Rogge, Christoph Zoerr, Simon Erhard i Jan Philipp Schmidt. "Physics-Informed Neural Networks for State of Health Estimation in Lithium-Ion Batteries". Journal of The Electrochemical Society 170, nr 9 (1.09.2023): 090524. http://dx.doi.org/10.1149/1945-7111/acf0ef.
Pełny tekst źródłaKarakonstantis, Xenofon, Diego Caviedes-Nozal, Antoine Richard i Efren Fernandez-Grande. "Room impulse response reconstruction with physics-informed deep learning". Journal of the Acoustical Society of America 155, nr 2 (1.02.2024): 1048–59. http://dx.doi.org/10.1121/10.0024750.
Pełny tekst źródłaKenzhebek, Y., T. S. Imankulov i D. Zh Akhmed-Zaki. "PREDICTION OF OIL PRODUCTION USING PHYSICS-INFORMED NEURAL NETWORKS". BULLETIN Series of Physics & Mathematical Sciences 76, nr 4 (15.12.2021): 45–50. http://dx.doi.org/10.51889/2021-4.1728-7901.06.
Pełny tekst źródłaPu, Ruilong, i Xinlong Feng. "Physics-Informed Neural Networks for Solving Coupled Stokes–Darcy Equation". Entropy 24, nr 8 (11.08.2022): 1106. http://dx.doi.org/10.3390/e24081106.
Pełny tekst źródłaYoon, Seunghyun, Yongsung Park i Woojae Seong. "Improving mode extraction with physics-informed neural network". Journal of the Acoustical Society of America 154, nr 4_supplement (1.10.2023): A339—A340. http://dx.doi.org/10.1121/10.0023729.
Pełny tekst źródłaStenkin, Dmitry, i Vladimir Gorbachenko. "Mathematical Modeling on a Physics-Informed Radial Basis Function Network". Mathematics 12, nr 2 (11.01.2024): 241. http://dx.doi.org/10.3390/math12020241.
Pełny tekst źródłaSchmid, Johannes D., Philipp Bauerschmidt, Caglar Gurbuz i Steffen Marburg. "Physics-informed neural networks for characterization of structural dynamic boundary conditions". Journal of the Acoustical Society of America 154, nr 4_supplement (1.10.2023): A99. http://dx.doi.org/10.1121/10.0022923.
Pełny tekst źródłaZhai, Hanfeng, Quan Zhou i Guohui Hu. "Predicting micro-bubble dynamics with semi-physics-informed deep learning". AIP Advances 12, nr 3 (1.03.2022): 035153. http://dx.doi.org/10.1063/5.0079602.
Pełny tekst źródłaKarakonstantis, Xenofon, i Efren Fernandez-Grande. "Advancing sound field analysis with physics-informed neural networks". Journal of the Acoustical Society of America 154, nr 4_supplement (1.10.2023): A98. http://dx.doi.org/10.1121/10.0022920.
Pełny tekst źródłaPannekoucke, Olivier, i Ronan Fablet. "PDE-NetGen 1.0: from symbolic partial differential equation (PDE) representations of physical processes to trainable neural network representations". Geoscientific Model Development 13, nr 7 (30.07.2020): 3373–82. http://dx.doi.org/10.5194/gmd-13-3373-2020.
Pełny tekst źródłaSchmid, Johannes. "Physics-informed neural networks for solving the Helmholtz equation". INTER-NOISE and NOISE-CON Congress and Conference Proceedings 267, nr 1 (5.11.2023): 265–68. http://dx.doi.org/10.3397/no_2023_0049.
Pełny tekst źródłaYoon, Seunghyun, Yongsung Park, Peter Gerstoft i Woojae Seong. "Predicting ocean pressure field with a physics-informed neural network". Journal of the Acoustical Society of America 155, nr 3 (1.03.2024): 2037–49. http://dx.doi.org/10.1121/10.0025235.
Pełny tekst źródłaYoon, Seunghyun, Yongsung Park, Peter Gerstoft i Woojae Seong. "Physics-informed neural network for predicting unmeasured ocean acoustic pressure field". Journal of the Acoustical Society of America 154, nr 4_supplement (1.10.2023): A97. http://dx.doi.org/10.1121/10.0022916.
Pełny tekst źródłaHassanaly, Malik, Peter J. Weddle, Kandler Smith, Subhayan De, Alireza Doostan i Ryan King. "Physics-Informed Neural Network Modeling of Li-Ion Batteries". ECS Meeting Abstracts MA2022-02, nr 3 (9.10.2022): 174. http://dx.doi.org/10.1149/ma2022-023174mtgabs.
Pełny tekst źródłaGuler Bayazit, Nilgun. "Physics informed neural network consisting of two decoupled stages". Engineering Science and Technology, an International Journal 46 (październik 2023): 101489. http://dx.doi.org/10.1016/j.jestch.2023.101489.
Pełny tekst źródłaSha, Yanliang, Jun Lan, Yida Li i Quan Chen. "A Physics-Informed Recurrent Neural Network for RRAM Modeling". Electronics 12, nr 13 (2.07.2023): 2906. http://dx.doi.org/10.3390/electronics12132906.
Pełny tekst źródłaLiu, Chen-Xu, Xinghao Wang, Weiming Liu, Yi-Fan Yang, Gui-Lan Yu i Zhanli Liu. "A physics-informed neural network for Kresling origami structures". International Journal of Mechanical Sciences 269 (maj 2024): 109080. http://dx.doi.org/10.1016/j.ijmecsci.2024.109080.
Pełny tekst źródłaOlivieri, Marco, Mirco Pezzoli, Fabio Antonacci i Augusto Sarti. "A Physics-Informed Neural Network Approach for Nearfield Acoustic Holography". Sensors 21, nr 23 (25.11.2021): 7834. http://dx.doi.org/10.3390/s21237834.
Pełny tekst źródłaHooshyar, Saman, i Arash Elahi. "Sequencing Initial Conditions in Physics-Informed Neural Networks". Journal of Chemistry and Environment 3, nr 1 (26.03.2024): 98–108. http://dx.doi.org/10.56946/jce.v3i1.345.
Pełny tekst źródłaOluwasakin, Ebenezer O., i Abdul Q. M. Khaliq. "Optimizing Physics-Informed Neural Network in Dynamic System Simulation and Learning of Parameters". Algorithms 16, nr 12 (28.11.2023): 547. http://dx.doi.org/10.3390/a16120547.
Pełny tekst źródłaLiu, Zhixiang, Yuanji Chen, Ge Song, Wei Song i Jingxiang Xu. "Combination of Physics-Informed Neural Networks and Single-Relaxation-Time Lattice Boltzmann Method for Solving Inverse Problems in Fluid Mechanics". Mathematics 11, nr 19 (1.10.2023): 4147. http://dx.doi.org/10.3390/math11194147.
Pełny tekst źródłaSilva, Roberto Mamud Guedes da, Helio dos Santos Migon i Antônio José da Silva Neto. "Parameter estimation in the pollutant dispersion problem with Physics-Informed Neural Networks". Ciência e Natura 45, esp. 3 (1.12.2023): e74615. http://dx.doi.org/10.5902/2179460x74615.
Pełny tekst źródłaLi, Jianfeng, Liangying Zhou, Jingwei Sun i Guangzhong Sun. "Physically plausible and conservative solutions to Navier-Stokes equations using Physics-Informed CNNs". JUSTC 53 (2023): 1. http://dx.doi.org/10.52396/justc-2022-0174.
Pełny tekst źródłaTarkhov, Dmitriy, Tatiana Lazovskaya i 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, nr 2 (6.01.2023): 663. http://dx.doi.org/10.3390/s23020663.
Pełny tekst źródłaLeung, Wing Tat, Guang Lin i Zecheng Zhang. "NH-PINN: Neural homogenization-based physics-informed neural network for multiscale problems". Journal of Computational Physics 470 (grudzień 2022): 111539. http://dx.doi.org/10.1016/j.jcp.2022.111539.
Pełny tekst źródłaAntonion, Klapa, Xiao Wang, Maziar Raissi i Laurn Joshie. "Machine Learning Through Physics–Informed Neural Networks: Progress and Challenges". Academic Journal of Science and Technology 9, nr 1 (20.01.2024): 46–49. http://dx.doi.org/10.54097/b1d21816.
Pełny tekst źródłaCao, Fujun, Xiaobin Guo, Fei Gao i Dongfang Yuan. "Deep Learning Nonhomogeneous Elliptic Interface Problems by Soft Constraint Physics-Informed Neural Networks". Mathematics 11, nr 8 (13.04.2023): 1843. http://dx.doi.org/10.3390/math11081843.
Pełny tekst źródłaGrubas, Serafim I., Sergey V. Yaskevich i Anton A. Duchkov. "LOCALIZATION OF MICROSEISMIC EVENTS USING PHYSICS-INFORMED NEURAL NETWORK SOLUTION TO THE EIKONAL EQUATION". Interexpo GEO-Siberia 2, nr 2 (21.05.2021): 32–38. http://dx.doi.org/10.33764/2618-981x-2021-2-2-32-38.
Pełny tekst źródłaZhi, Peng, Yuching Wu, Cheng Qi, Tao Zhu, Xiao Wu i Hongyu Wu. "Surrogate-Based Physics-Informed Neural Networks for Elliptic Partial Differential Equations". Mathematics 11, nr 12 (15.06.2023): 2723. http://dx.doi.org/10.3390/math11122723.
Pełny tekst źródłaRafiq, Muhammad, Ghazala Rafiq i 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.
Pełny tekst źródłaRazakh, Taufeq Mohammed, Beibei Wang, Shane Jackson, Rajiv K. Kalia, Aiichiro Nakano, Ken-ichi Nomura i Priya Vashishta. "PND: Physics-informed neural-network software for molecular dynamics applications". SoftwareX 15 (lipiec 2021): 100789. http://dx.doi.org/10.1016/j.softx.2021.100789.
Pełny tekst źródłaJi, Weiqi, Weilun Qiu, Zhiyu Shi, Shaowu Pan i Sili Deng. "Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics". Journal of Physical Chemistry A 125, nr 36 (31.08.2021): 8098–106. http://dx.doi.org/10.1021/acs.jpca.1c05102.
Pełny tekst źródłaChakraborty, Souvik. "Transfer learning based multi-fidelity physics informed deep neural network". Journal of Computational Physics 426 (luty 2021): 109942. http://dx.doi.org/10.1016/j.jcp.2020.109942.
Pełny tekst źródłaMeng, Xuhui, Zhen Li, Dongkun Zhang i George Em Karniadakis. "PPINN: Parareal physics-informed neural network for time-dependent PDEs". Computer Methods in Applied Mechanics and Engineering 370 (październik 2020): 113250. http://dx.doi.org/10.1016/j.cma.2020.113250.
Pełny tekst źródłaDalton, David, Dirk Husmeier i Hao Gao. "Physics-informed graph neural network emulation of soft-tissue mechanics". Computer Methods in Applied Mechanics and Engineering 417 (grudzień 2023): 116351. http://dx.doi.org/10.1016/j.cma.2023.116351.
Pełny tekst źródłaLiu, Yu, i Wentao Ma. "Gradient auxiliary physics-informed neural network for nonlinear biharmonic equation". Engineering Analysis with Boundary Elements 157 (grudzień 2023): 272–82. http://dx.doi.org/10.1016/j.enganabound.2023.09.013.
Pełny tekst źródłaDu, Meiyuan, Chi Zhang, Sheng Xie, Fang Pu, Da Zhang i Deyu Li. "Investigation on aortic hemodynamics based on physics-informed neural network". Mathematical Biosciences and Engineering 20, nr 7 (2023): 11545–67. http://dx.doi.org/10.3934/mbe.2023512.
Pełny tekst źródłaNgo, Quang-Ha, Bang L. H. Nguyen, Tuyen V. Vu, Jianhua Zhang i Tuan Ngo. "Physics-informed graphical neural network for power system state estimation". Applied Energy 358 (marzec 2024): 122602. http://dx.doi.org/10.1016/j.apenergy.2023.122602.
Pełny tekst źródłama, fei, Sipei Zhao i Thushara Abhayapala. "Physics-informed neural network assisted spherical microphone array signal processing". Journal of the Acoustical Society of America 154, nr 4_supplement (1.10.2023): A182. http://dx.doi.org/10.1121/10.0023200.
Pełny tekst źródłaYin, Jichao, Ziming Wen, Shuhao Li, Yaya Zhang i Hu Wang. "Dynamically configured physics-informed neural network in topology optimization applications". Computer Methods in Applied Mechanics and Engineering 426 (czerwiec 2024): 117004. http://dx.doi.org/10.1016/j.cma.2024.117004.
Pełny tekst źródłaXypakis, Emmanouil, Valeria deTurris, Fabrizio Gala, Giancarlo Ruocco i Marco Leonetti. "Physics-informed machine learning for microscopy". EPJ Web of Conferences 266 (2022): 04007. http://dx.doi.org/10.1051/epjconf/202226604007.
Pełny tekst źródłaLee, Brandon M., i David R. Dowling. "Training physics-informed neural networks to directly predict acoustic field values in simple environments". Journal of the Acoustical Society of America 152, nr 4 (październik 2022): A49. http://dx.doi.org/10.1121/10.0015499.
Pełny tekst źródłaZhang, Guangtao, Huiyu Yang, Guanyu Pan, Yiting Duan, Fang Zhu i Yang Chen. "Constrained Self-Adaptive Physics-Informed Neural Networks with ResNet Block-Enhanced Network Architecture". Mathematics 11, nr 5 (22.02.2023): 1109. http://dx.doi.org/10.3390/math11051109.
Pełny tekst źródłaUsama, Muhammad, Rui Ma, Jason Hart i Mikaela Wojcik. "Physics-Informed Neural Networks (PINNs)-Based Traffic State Estimation: An Application to Traffic Network". Algorithms 15, nr 12 (27.11.2022): 447. http://dx.doi.org/10.3390/a15120447.
Pełny tekst źródłaSilva Garzon, Camilo Fernando, Philip Bonnaire, Nguyen Anh Khoa Doan, Korbinian Niebler i Camilo Fernando Silva. "Towards reconstruction of acoustic fields via physics-informed neural networks". INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, nr 3 (1.02.2023): 4773–82. http://dx.doi.org/10.3397/in_2022_0690.
Pełny tekst źródłaManikkan, Sreehari, i Balaji Srinivasan. "Transfer physics informed neural network: a new framework for distributed physics informed neural networks via parameter sharing". Engineering with Computers, 19.07.2022. http://dx.doi.org/10.1007/s00366-022-01703-9.
Pełny tekst źródłaDourado, Arinan, i Felipe A. C. Viana. "Physics-Informed Neural Networks for Corrosion-Fatigue Prognosis". Annual Conference of the PHM Society 11, nr 1 (22.09.2019). http://dx.doi.org/10.36001/phmconf.2019.v11i1.814.
Pełny tekst źródłaLiu Jin-Pin, Wang Bing-Zhong, Chen Chuan-Sheng i Wang Ren. "Inverse design of microwave waveguide devices based on deep physics-informed neural networks". Acta Physica Sinica, 2023, 0. http://dx.doi.org/10.7498/aps.72.20230031.
Pełny tekst źródłaZapf, Bastian, Johannes Haubner, Miroslav Kuchta, Geir Ringstad, Per Kristian Eide i Kent-Andre Mardal. "Investigating molecular transport in the human brain from MRI with physics-informed neural networks". Scientific Reports 12, nr 1 (14.09.2022). http://dx.doi.org/10.1038/s41598-022-19157-w.
Pełny tekst źródłaFang Bo-Lang, Wang Jian-Guo i Feng GuoBin. "Centroid prediction using physics informed neural networks". Acta Physica Sinica, 2022, 0. http://dx.doi.org/10.7498/aps.71.20220670.
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