Academic literature on the topic 'Physics-Informed neural network'
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Journal articles on the topic "Physics-Informed neural network"
Hofmann, 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 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 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 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 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 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 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 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 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 textDissertations / Theses on the topic "Physics-Informed neural network"
Mirzai, Badi. "Physics-Informed Deep Learning for System Identification of Autonomous Underwater Vehicles : A Lagrangian Neural Network Approach." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301626.
Full textI den här uppsatsen utforskas Lagrangianska Neurala Nätverk (LNN) för systemidentifiering av Autonoma Undervattensfordon (AUV) med 6 frihetsgrader. En av de största utmaningarna med AUV är deras begränsningar när det kommer till trådlös kommunikation och navigering under vatten. Ett krav för att ha fungerande AUV är deras förmåga att navigera och utföra uppdrag under okända undervattensförhållanden med begränsad och brusig sensordata. Dessutom är ett kritiskt krav för lokalisering och adaptiv reglerteknik att ha noggranna modeller av systemets olinjära dynamik, samtidigt som den dynamiska miljön i havet tas i beaktande. De flesta sådana modeller tar inte i beaktande sensordata för att reglera dess parameterar. Insamling av sådan data för AUVer är besvärligt, men nödvändigt för att skapa större flexibilitet hos modellens parametrar. Trots de senaste genombrotten inom djupinlärning är traditionella metoder av systemidentifiering dominanta än idag för AUV. Det är av dessa anledningar som vi i denna uppsats strävar efter en datadriven metod, där vi förankrar lagar från fysik under inlärningen av systemets state-space modell. Mer specifikt utforskar vi LNN för ett system med högre dimension. Vidare expanderar vi även LNN till att även ta ickekonservativa krafter som verkar på systemet i beaktande, såsom dämpning och styrsignaler. Nätverket tränas att lära sig från simulerad data från en andra ordningens differentialekvation som beskriver en AUV. Den tränade modellen utvärderas genom att iterativt integrera fram dess rörelse från olika initialstillstånd, vilket jämförs med den korrekta modellen. Resultaten visade en modell som till viss del var kapabel till att förutspå korrekt acceleration, med begränsad framgång i att lära sig korrekt rörelseriktning framåt i tiden.
Cedergren, Linnéa. "Physics-informed Neural Networks for Biopharma Applications." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-185423.
Full textWu, Dawen. "Solving Some Nonlinear Optimization Problems with Deep Learning." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG083.
Full textThis thesis considers four types of nonlinear optimization problems, namely bimatrix games, nonlinear projection equations (NPEs), nonsmooth convex optimization problems (NCOPs), and chance-constrained games (CCGs).These four classes of nonlinear optimization problems find extensive applications in various domains such as engineering, computer science, economics, and finance.We aim to introduce deep learning-based algorithms to efficiently compute the optimal solutions for these nonlinear optimization problems.For bimatrix games, we use Convolutional Neural Networks (CNNs) to compute Nash equilibria.Specifically, we design a CNN architecture where the input is a bimatrix game and the output is the predicted Nash equilibrium for the game.We generate a set of bimatrix games by a given probability distribution and use the Lemke-Howson algorithm to find their true Nash equilibria, thereby constructing a training dataset.The proposed CNN is trained on this dataset to improve its accuracy. Upon completion of training, the CNN is capable of predicting Nash equilibria for unseen bimatrix games.Experimental results demonstrate the exceptional computational efficiency of our CNN-based approach, at the cost of sacrificing some accuracy.For NPEs, NCOPs, and CCGs, which are more complex optimization problems, they cannot be directly fed into neural networks.Therefore, we resort to advanced tools, namely neurodynamic optimization and Physics-Informed Neural Networks (PINNs), for solving these problems.Specifically, we first use a neurodynamic approach to model a nonlinear optimization problem as a system of Ordinary Differential Equations (ODEs).Then, we utilize a PINN-based model to solve the resulting ODE system, where the end state of the model represents the predicted solution to the original optimization problem.The neural network is trained toward solving the ODE system, thereby solving the original optimization problem.A key contribution of our proposed method lies in transforming a nonlinear optimization problem into a neural network training problem.As a result, we can now solve nonlinear optimization problems using only PyTorch, without relying on classical convex optimization solvers such as CVXPY, CPLEX, or Gurobi
(8828960), Sukirt. "Physics Informed Neural Networks for Engineering Systems." Thesis, 2020.
Find full textYadav, Sangeeta. "Data Driven Stabilization Schemes for Singularly Perturbed Differential Equations." Thesis, 2023. https://etd.iisc.ac.in/handle/2005/6095.
Full text(10141679), Haoyang Zheng. "Quantifying implicit and explicit constraints on physics-informed neural processes." Thesis, 2021.
Find full textDue to strong interactions among various phases and among the phases and fluid motions, multiphase flows (MPFs) are so complex that lots of efforts have to be paid to predict its sequential patterns of phases and motions. The present paper applies the physical constraints inherent in MPFs and enforces them to a physics-informed neural network (PINN) model either explicitly or implicitly, depending on the type of constraints. To predict the unobserved order parameters (OPs) (which locate the phases) in the future steps, the conditional neural processes (CNPs) with long short-term memory (LSTM, combined as CNPLSTM) are applied to quickly infer the dynamics of the phases after encoding only a few observations. After that, the multiphase consistent and conservative boundedness mapping (MCBOM) algorithm is implemented the correction the predicted OPs from CNP-LSTM so that the mass conservation, the summation of the volume fractions of the phases being unity, the consistency of reduction, and the boundedness of the OPs are strictly satisfied. Next, the density of the fluid mixture is computed from the corrected OPs. The observed velocity and density of the fluid mixture then encode in a physics-informed conditional neural processes and long short-term memory (PICNP-LSTM) where the constraint of momentum conservation is included in the loss function. Finally, the unobserved velocity in future steps is predicted from PICNP-LSTM. The proposed physics-informed neural processes (PINPs) model (CNP-LSTM-MCBOM-PICNP-LSTM) for MPFs avoids unphysical behaviors of the OPs, accelerates the convergence, and requires fewer data. The proposed model successfully predicts several canonical MPF problems, i.e., the horizontal shear layer (HSL) and dam break (DB) problems, and its performances are validated.
Alhubail, Ali. "Application of Physics-Informed Neural Networks to Solve 2-D Single-phase Flow in Heterogeneous Porous Media." Thesis, 2021. http://hdl.handle.net/10754/670174.
Full textBook chapters on the topic "Physics-Informed neural network"
Madenci, Erdogan, Pranesh Roy, and Deepak Behera. "Peridynamics for Physics Informed Neural Network." In Advances in Peridynamics, 399–418. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97858-7_16.
Full textBeniwal, Kirti, and Vivek Kumar. "Gradient-Based Physics-Informed Neural Network." In Third Congress on Intelligent Systems, 749–61. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9379-4_54.
Full textDhamirah Mohamad, Najwa Zawani, Akram Yousif, Nasiha Athira Binti Shaari, Hasreq Iskandar Mustafa, Samsul Ariffin Abdul Karim, Afza Shafie, and Muhammad Izzatullah. "Heat Transfer Modelling with Physics-Informed Neural Network (PINN)." In Studies in Systems, Decision and Control, 25–35. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04028-3_3.
Full textMahesh, Ragini Bal, Jorge Leandro, and Qing Lin. "Physics Informed Neural Network for Spatial-Temporal Flood Forecasting." In Lecture Notes in Civil Engineering, 77–91. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5501-2_7.
Full textOh, Dong Keun. "Pure Physics-Informed Echo State Network of ODE Solution Replicator." In Artificial Neural Networks and Machine Learning – ICANN 2023, 225–36. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44201-8_19.
Full textIbrahim, Abdul Qadir, Sebastian Götschel, and Daniel Ruprecht. "Parareal with a Physics-Informed Neural Network as Coarse Propagator." In Euro-Par 2023: Parallel Processing, 649–63. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39698-4_44.
Full textSadouk, Lamyaa, Mohamed ElHassan Bassir, Ibrahim Bassir, and Boujemâa Achchab. "Physics-Informed Neural Network with PDE Soft Constraint Regularization Invariance." In Advances in Intelligent System and Smart Technologies, 315–26. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-47672-3_31.
Full textFallah, Ali, and Mohammad Mohammadi Aghdam. "Physics-Informed Neural Network for Solution of Nonlinear Differential Equations." In Nonlinear Approaches in Engineering Application, 163–78. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53582-6_5.
Full textXie, Baihong, Xiujian Liu, Heye Zhang, Chenchu Xu, Tieyong Zeng, Yixuan Yuan, Guang Yang, and Zhifan Gao. "Conditional Physics-Informed Graph Neural Network for Fractional Flow Reserve Assessment." In Lecture Notes in Computer Science, 110–20. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43990-2_11.
Full textZarzycki, Krzysztof, and Maciej Ławryńczuk. "Physics-Informed Hybrid Neural Network Model for MPC: A Fuzzy Approach." In Advanced, Contemporary Control, 183–92. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35170-9_17.
Full textConference papers on the topic "Physics-Informed neural network"
Qin, Jingtao, and Nanpeng Yu. "Reconfigure Distribution Network with Physics-informed Graph Neural Network." In 2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE). IEEE, 2023. http://dx.doi.org/10.1109/isgteurope56780.2023.10407802.
Full textNakamura, Yo, Suguru Shiratori, Hideaki Nagano, and Kenjiro Shimano. "Physics-Informed Neural Network with Variable Initial Conditions." In 7th World Congress on Mechanical, Chemical, and Material Engineering. Avestia Publishing, 2021. http://dx.doi.org/10.11159/htff21.113.
Full textLim, Kart Leong, Rahul Dutta, and Mihai Rotaru. "Physics Informed Neural Network using Finite Difference Method." In 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2022. http://dx.doi.org/10.1109/smc53654.2022.9945171.
Full textSha, Yanliang, Lingyun Ouyang, and Quan Chen. "A Physics-Informed Neural Network for RRAM modeling." In 2021 International Applied Computational Electromagnetics Society (ACES-China) Symposium. IEEE, 2021. http://dx.doi.org/10.23919/aces-china52398.2021.9581858.
Full textSu, Yawei, Shubin Zeng, Xuqing Wu, Yueqin Huang, and Jiefu Chen. "Physics-Informed Graph Neural Network for Electromagnetic Simulations." In 2023 XXXVth General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS). IEEE, 2023. http://dx.doi.org/10.23919/ursigass57860.2023.10265621.
Full textAshqar, Farah, Rakan Khoury, Caroline Wood, Yi-Hsuan Yeh, Aristeidis Seretis, and Costas D. Sarris. "Physics-Informed Convolutional Neural Network for Indoor Localization." In 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI). IEEE, 2021. http://dx.doi.org/10.1109/aps/ursi47566.2021.9704309.
Full textNair, Siddharth, Timothy F. Walsh, Greg Pickrell, and Fabio Semperlotti. "Acoustic scattering simulations via physics-informed neural network." In Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2024, edited by Maria Pina Limongelli, Ching Tai Ng, and Branko Glisic. SPIE, 2024. http://dx.doi.org/10.1117/12.3010166.
Full textPérez, José, Rafael Baez, Jose Terrazas, Arturo Rodríguez, Daniel Villanueva, Olac Fuentes, Vinod Kumar, Brandon Paez, and Abdiel Cruz. "Physics-Informed Long-Short Term Memory Neural Network Performance on Holloman High-Speed Test Track Sled Study." In ASME 2022 Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/fedsm2022-86953.
Full textAlmeldein, Ahmed, and Noah Van Dam. "Accelerating Chemical Kinetics Calculations With Physics Informed Neural Networks." In ASME 2022 ICE Forward Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/icef2022-90371.
Full textVillanueva, Daniel, Brandon Paez, Arturo Rodriguez, Ashesh Chattopadhyay, V. M. Krushnarao Kotteda, Rafael Baez, Jose Perez, Jose Terrazas, and Vinod Kumar. "Field Predictions of Hypersonic Cones Using Physics-Informed Neural Networks." In ASME 2022 Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/fedsm2022-86957.
Full textReports on the topic "Physics-Informed neural network"
Ellis, Kai, Nilanjan Banerjee, and Christopher Pierce. Modeling a Thermionic Electron Source Using a Physics-Informed Neural Network. Office of Scientific and Technical Information (OSTI), October 2023. http://dx.doi.org/10.2172/2008057.
Full textPettit, Chris, and D. Wilson. A physics-informed neural network for sound propagation in the atmospheric boundary layer. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41034.
Full textNadiga, Balasubramanya, and Robert Lowrie. Physics Informed Neural Networks as Computational Physics Emulators. Office of Scientific and Technical Information (OSTI), June 2023. http://dx.doi.org/10.2172/1985825.
Full textD'Elia, Marta, Michael L. Parks, Guofei Pang, and George Karniadakis. nPINNs: nonlocal Physics-Informed Neural Networks for a parametrized nonlocal universal Laplacian operator. Algorithms and Applications. Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1614899.
Full textSECOND-ORDER ANALYSIS OF BEAM-COLUMNS BY MACHINE LEARNING-BASED STRUCTURAL ANALYSIS THROUGH PHYSICS-INFORMED NEURAL NETWORKS. The Hong Kong Institute of Steel Construction, December 2023. http://dx.doi.org/10.18057/ijasc.2023.19.4.10.
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