Journal articles on the topic 'Physics-Informed neural network'
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Trahan, Corey, Mark Loveland, and Samuel Dent. "Quantum Physics-Informed Neural Networks." Entropy 26, no. 8 (July 30, 2024): 649. http://dx.doi.org/10.3390/e26080649.
Full textWang, Jing, Yubo Li, Anping Wu, Zheng Chen, Jun Huang, Qingfeng Wang, and Feng Liu. "Multi-Step Physics-Informed Deep Operator Neural Network for Directly Solving Partial Differential Equations." Applied Sciences 14, no. 13 (June 25, 2024): 5490. http://dx.doi.org/10.3390/app14135490.
Full textHofmann, Tobias, Jacob Hamar, Marcel Rogge, Christoph Zoerr, Simon Erhard, and Jan Philipp Schmidt. "Physics-Informed Neural Networks for State of Health Estimation in Lithium-Ion Batteries." Journal of The Electrochemical Society 170, no. 9 (September 1, 2023): 090524. http://dx.doi.org/10.1149/1945-7111/acf0ef.
Full textLi, Zhenyu. "A Review of Physics-Informed Neural Networks." Applied and Computational Engineering 133, no. 1 (January 24, 2025): 165–73. https://doi.org/10.54254/2755-2721/2025.20636.
Full textKarakonstantis, Xenofon, Diego Caviedes-Nozal, Antoine Richard, and Efren Fernandez-Grande. "Room impulse response reconstruction with physics-informed deep learning." Journal of the Acoustical Society of America 155, no. 2 (February 1, 2024): 1048–59. http://dx.doi.org/10.1121/10.0024750.
Full textPu, Ruilong, and Xinlong Feng. "Physics-Informed Neural Networks for Solving Coupled Stokes–Darcy Equation." Entropy 24, no. 8 (August 11, 2022): 1106. http://dx.doi.org/10.3390/e24081106.
Full textKenzhebek, 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.
Full textHou, Shubo, Wenchao Wu, and Xiuhong Hao. "Physics-informed neural network for simulating magnetic field of permanent magnet." Journal of Physics: Conference Series 2853, no. 1 (October 1, 2024): 012018. http://dx.doi.org/10.1088/1742-6596/2853/1/012018.
Full textYoon, Seunghyun, Yongsung Park, and Woojae Seong. "Improving mode extraction with physics-informed neural network." Journal of the Acoustical Society of America 154, no. 4_supplement (October 1, 2023): A339—A340. http://dx.doi.org/10.1121/10.0023729.
Full textPan, Cunliang, Shi Feng, Shengyang Tao, Hongwu Zhang, Yonggang Zheng, and Hongfei Ye. "Physics-Informed Neural Network for Young-Laplace Equation." International Conference on Computational & Experimental Engineering and Sciences 30, no. 1 (2024): 1. http://dx.doi.org/10.32604/icces.2024.011132.
Full textSchmid, Johannes D., Philipp Bauerschmidt, Caglar Gurbuz, and Steffen Marburg. "Physics-informed neural networks for characterization of structural dynamic boundary conditions." Journal of the Acoustical Society of America 154, no. 4_supplement (October 1, 2023): A99. http://dx.doi.org/10.1121/10.0022923.
Full textStenkin, Dmitry, and Vladimir Gorbachenko. "Mathematical Modeling on a Physics-Informed Radial Basis Function Network." Mathematics 12, no. 2 (January 11, 2024): 241. http://dx.doi.org/10.3390/math12020241.
Full textZhai, 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.
Full textKarakonstantis, Xenofon, and Efren Fernandez-Grande. "Advancing sound field analysis with physics-informed neural networks." Journal of the Acoustical Society of America 154, no. 4_supplement (October 1, 2023): A98. http://dx.doi.org/10.1121/10.0022920.
Full textVo, Van Truong, Samad Noeiaghdam, Denis Sidorov, Aliona Dreglea, and Liguo Wang. "Solving Nonlinear Energy Supply and Demand System Using Physics-Informed Neural Networks." Computation 13, no. 1 (January 8, 2025): 13. https://doi.org/10.3390/computation13010013.
Full textDong, Chenghao. "Solving Differential Equations with Physics-Informed Neural Networks." Theoretical and Natural Science 87, no. 1 (January 15, 2025): 137–46. https://doi.org/10.54254/2753-8818/2025.20346.
Full textKaliuzhniak, Anastasiia, Oleksii Kudi, Yuriy Belokon, and Dmytro Kruglyak. "Developing of neural network computing methods for solving inverse elasticity problems." Eastern-European Journal of Enterprise Technologies 6, no. 7 (132) (December 30, 2024): 45–52. https://doi.org/10.15587/1729-4061.2024.313795.
Full textPannekoucke, Olivier, and 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, no. 7 (July 30, 2020): 3373–82. http://dx.doi.org/10.5194/gmd-13-3373-2020.
Full textSchmid, Johannes. "Physics-informed neural networks for solving the Helmholtz equation." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 267, no. 1 (November 5, 2023): 265–68. http://dx.doi.org/10.3397/no_2023_0049.
Full textYoon, Seunghyun, Yongsung Park, Peter Gerstoft, and Woojae Seong. "Physics-informed neural network for predicting unmeasured ocean acoustic pressure field." Journal of the Acoustical Society of America 154, no. 4_supplement (October 1, 2023): A97. http://dx.doi.org/10.1121/10.0022916.
Full textYoon, Seunghyun, Yongsung Park, Peter Gerstoft, and Woojae Seong. "Predicting ocean pressure field with a physics-informed neural network." Journal of the Acoustical Society of America 155, no. 3 (March 1, 2024): 2037–49. http://dx.doi.org/10.1121/10.0025235.
Full textHassanaly, Malik, Peter J. Weddle, Corey R. Randall, Eric J. Dufek, and Kandler Smith. "Rapid Inverse Parameter Inference Using Physics-Informed Neural Networks." ECS Meeting Abstracts MA2024-01, no. 2 (August 9, 2024): 345. http://dx.doi.org/10.1149/ma2024-012345mtgabs.
Full textHassanaly, 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.
Full textGuler Bayazit, Nilgun. "Physics informed neural network consisting of two decoupled stages." Engineering Science and Technology, an International Journal 46 (October 2023): 101489. http://dx.doi.org/10.1016/j.jestch.2023.101489.
Full textSha, Yanliang, Jun Lan, Yida Li, and Quan Chen. "A Physics-Informed Recurrent Neural Network for RRAM Modeling." Electronics 12, no. 13 (July 2, 2023): 2906. http://dx.doi.org/10.3390/electronics12132906.
Full textLiu, Chen-Xu, Xinghao Wang, Weiming Liu, Yi-Fan Yang, Gui-Lan Yu, and Zhanli Liu. "A physics-informed neural network for Kresling origami structures." International Journal of Mechanical Sciences 269 (May 2024): 109080. http://dx.doi.org/10.1016/j.ijmecsci.2024.109080.
Full textCosta, Erbet Almeida, Carine Menezes Rebello, Vinícius Viena Santana, and Idelfonso B. R. Nogueira. "Physics-informed neural network uncertainty assessment through Bayesian inference." IFAC-PapersOnLine 58, no. 14 (2024): 652–57. http://dx.doi.org/10.1016/j.ifacol.2024.08.411.
Full textShi, Yan, and Michael Beer. "Physics-informed neural network classification framework for reliability analysis." Expert Systems with Applications 258 (December 2024): 125207. http://dx.doi.org/10.1016/j.eswa.2024.125207.
Full textCai, Zemin, Xiangqi Lin, Tianshu Liu, Fan Wu, Shizhao Wang, and Yun Liu. "Determining pressure from velocity via physics-informed neural network." European Journal of Mechanics - B/Fluids 109 (January 2025): 1–21. http://dx.doi.org/10.1016/j.euromechflu.2024.08.007.
Full textLiu, Xue, Wei Cheng, Ji Xing, Xuefeng Chen, Zhibin Zhao, Rongyong Zhang, Qian Huang, et al. "Physics-informed Neural Network for system identification of rotors." IFAC-PapersOnLine 58, no. 15 (2024): 307–12. http://dx.doi.org/10.1016/j.ifacol.2024.08.546.
Full textBerrone, Stefano, and Moreno Pintore. "Meshfree Variational-Physics-Informed Neural Networks (MF-VPINN): An Adaptive Training Strategy." Algorithms 17, no. 9 (September 19, 2024): 415. http://dx.doi.org/10.3390/a17090415.
Full textPonomarev, R. Yu, R. R. Ziazev, A. A. Leshchenko, R. R. Migmanov, and M. I. Ivlev. "Flooding system optimization: Advantages of a hybrid approach to developing neural network filtration models." Actual Problems of Oil and Gas 15, no. 4 (December 29, 2024): 349–63. https://doi.org/10.29222/ipng.2078-5712.2024-15-4.art3.
Full textLiu, Zhixiang, Yuanji Chen, Ge Song, Wei Song, and Jingxiang Xu. "Combination of Physics-Informed Neural Networks and Single-Relaxation-Time Lattice Boltzmann Method for Solving Inverse Problems in Fluid Mechanics." Mathematics 11, no. 19 (October 1, 2023): 4147. http://dx.doi.org/10.3390/math11194147.
Full textHooshyar, Saman, and Arash Elahi. "Sequencing Initial Conditions in Physics-Informed Neural Networks." Journal of Chemistry and Environment 3, no. 1 (March 26, 2024): 98–108. http://dx.doi.org/10.56946/jce.v3i1.345.
Full textSilva, Roberto Mamud Guedes da, Helio dos Santos Migon, and Antônio José da Silva Neto. "Parameter estimation in the pollutant dispersion problem with Physics-Informed Neural Networks." Ciência e Natura 45, esp. 3 (December 1, 2023): e74615. http://dx.doi.org/10.5902/2179460x74615.
Full textOluwasakin, Ebenezer O., and Abdul Q. M. Khaliq. "Optimizing Physics-Informed Neural Network in Dynamic System Simulation and Learning of Parameters." Algorithms 16, no. 12 (November 28, 2023): 547. http://dx.doi.org/10.3390/a16120547.
Full textTarkhov, 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.
Full textOlivieri, Marco, Mirco Pezzoli, Fabio Antonacci, and Augusto Sarti. "A Physics-Informed Neural Network Approach for Nearfield Acoustic Holography." Sensors 21, no. 23 (November 25, 2021): 7834. http://dx.doi.org/10.3390/s21237834.
Full textLi, Jianfeng, Liangying Zhou, Jingwei Sun, and 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.
Full textHariri, I., A. Radid, and K. Rhofir. "Physics-informed neural networks for the reaction-diffusion Brusselator model." Mathematical Modeling and Computing 11, no. 2 (2024): 448–54. http://dx.doi.org/10.23939/mmc2024.02.448.
Full textYonekura, Kazuo. "A Short Note on Physics-Guided GAN to Learn Physical Models without Gradients." Algorithms 17, no. 7 (June 26, 2024): 279. http://dx.doi.org/10.3390/a17070279.
Full textAntonion, Klapa, Xiao Wang, Maziar Raissi, and Laurn Joshie. "Machine Learning Through Physics–Informed Neural Networks: Progress and Challenges." Academic Journal of Science and Technology 9, no. 1 (January 20, 2024): 46–49. http://dx.doi.org/10.54097/b1d21816.
Full textLeung, 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.
Full textCao, Fujun, Xiaobin Guo, Fei Gao, and Dongfang Yuan. "Deep Learning Nonhomogeneous Elliptic Interface Problems by Soft Constraint Physics-Informed Neural Networks." Mathematics 11, no. 8 (April 13, 2023): 1843. http://dx.doi.org/10.3390/math11081843.
Full textSingh, Vishal, Dineshkumar Harursampath, Sharanjeet Dhawan, Manoj Sahni, Sahaj Saxena, and Rajnish Mallick. "Physics-Informed Neural Network for Solving a One-Dimensional Solid Mechanics Problem." Modelling 5, no. 4 (October 18, 2024): 1532–49. http://dx.doi.org/10.3390/modelling5040080.
Full textDuarte, D. H. G., P. D. S. de Lima, and J. M. de Araújo. "Outlier-resistant physics-informed neural network." Physical Review E 111, no. 2 (February 20, 2025). https://doi.org/10.1103/physreve.111.l023302.
Full textManikkan, Sreehari, and Balaji Srinivasan. "Transfer physics informed neural network: a new framework for distributed physics informed neural networks via parameter sharing." Engineering with Computers, July 19, 2022. http://dx.doi.org/10.1007/s00366-022-01703-9.
Full textLiu Jin-Pin, Wang Bing-Zhong, Chen Chuan-Sheng, and 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.
Full textDourado, Arinan, and Felipe A. C. Viana. "Physics-Informed Neural Networks for Corrosion-Fatigue Prognosis." Annual Conference of the PHM Society 11, no. 1 (September 22, 2019). http://dx.doi.org/10.36001/phmconf.2019.v11i1.814.
Full textZapf, Bastian, Johannes Haubner, Miroslav Kuchta, Geir Ringstad, Per Kristian Eide, and Kent-Andre Mardal. "Investigating molecular transport in the human brain from MRI with physics-informed neural networks." Scientific Reports 12, no. 1 (September 14, 2022). http://dx.doi.org/10.1038/s41598-022-19157-w.
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