Bibliografías temáticas / Physics-Informed neural network

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Literatura académica sobre el tema "Physics-Informed neural network"

Autor: Grafiati
Publicado: 25 de mayo de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Physics-Informed neural network"

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Trahan, Corey, Mark Loveland, and Samuel Dent. "Quantum Physics-Informed Neural Networks." Entropy 26, no. 8 (2024): 649. http://dx.doi.org/10.3390/e26080649.

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In this study, the PennyLane quantum device simulator was used to investigate quantum and hybrid, quantum/classical physics-informed neural networks (PINNs) for solutions to both transient and steady-state, 1D and 2D partial differential equations. The comparative expressibility of the purely quantum, hybrid and classical neural networks is discussed, and hybrid configurations are explored. The results show that (1) for some applications, quantum PINNs can obtain comparable accuracy with less neural network parameters than classical PINNs, and (2) adding quantum nodes in classical PINNs can in
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Wang, Jing, Yubo Li, Anping Wu, et al. "Multi-Step Physics-Informed Deep Operator Neural Network for Directly Solving Partial Differential Equations." Applied Sciences 14, no. 13 (2024): 5490. http://dx.doi.org/10.3390/app14135490.

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This paper establishes a method for solving partial differential equations using a multi-step physics-informed deep operator neural network. The network is trained by embedding physics-informed constraints. Different from traditional neural networks for solving partial differential equations, the proposed method uses a deep neural operator network to indirectly construct the mapping relationship between the variable functions and solution functions. This approach makes full use of the hidden information between the variable functions and independent variables. The process whereby the model cap
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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 (2023): 090524. http://dx.doi.org/10.1149/1945-7111/acf0ef.

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One of the most challenging tasks of modern battery management systems is the accurate state of health estimation. While physico-chemical models are accurate, they have high computational cost. Neural networks lack physical interpretability but are efficient. Physics-informed neural networks tackle the aforementioned shortcomings by combining the efficiency of neural networks with the accuracy of physico-chemical models. A physics-informed neural network is developed and evaluated against three different datasets: A pseudo-two-dimensional Newman model generates data at various state of health
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Pu, Ruilong, and Xinlong Feng. "Physics-Informed Neural Networks for Solving Coupled Stokes–Darcy Equation." Entropy 24, no. 8 (2022): 1106. http://dx.doi.org/10.3390/e24081106.

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In this paper, a grid-free deep learning method based on a physics-informed neural network is proposed for solving coupled Stokes–Darcy equations with Bever–Joseph–Saffman interface conditions. This method has the advantage of avoiding grid generation and can greatly reduce the amount of computation when solving complex problems. Although original physical neural network algorithms have been used to solve many differential equations, we find that the direct use of physical neural networks to solve coupled Stokes–Darcy equations does not provide accurate solutions in some cases, such as rigid t
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Li, Zhenyu. "A Review of Physics-Informed Neural Networks." Applied and Computational Engineering 133, no. 1 (2025): 165–73. https://doi.org/10.54254/2755-2721/2025.20636.

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This article presents Physics-Informed Neural Networks (PINNs), which integrate physical laws into neural network training to model complex systems governed by partial differential equations (PDEs). PINNs enhance data efficiency, allowing for accurate predictions with less training data, and have applications in fields such as biomedical engineering, geophysics, and material science. Despite their advantages, PINNs face challenges like learning high-frequency components and computational overhead. Proposed solutions include causality constraints and improved boundary condition handling. A nume
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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 (2021): 45–50. http://dx.doi.org/10.51889/2021-4.1728-7901.06.

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In recent years, modern information technologies have been actively used in various industries. The oil industry is no exception, since high-performance computing technologies, artificial intelligence algorithms, methods of collecting, processing and storing information are actively used to solve the problems of increasing oil recovery. Deep learning has made remarkable strides in a variety of applications, but its use for solving partial differential equations has only recently emerged. In particular, you can replace traditional numerical methods with a neural network that approximates the so
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Hou, 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 (2024): 012018. http://dx.doi.org/10.1088/1742-6596/2853/1/012018.

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Abstract With the rapid development of deep learning, its application in physical field simulation has been widely concerned, and it has begun to lead a new model of meshless simulation. In this paper, research based on physics-informed neural networks is carried out to solve partial differential equations related to the physical laws of electromagnetism. Then the magnetic field simulation is realized. In this method, the governing equation and the boundary conditions containing physical information are embedded into the neural network loss function as constraints, and the backpropagation of n
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Pan, 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.

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Yoon, 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 (2023): A339—A340. http://dx.doi.org/10.1121/10.0023729.

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This study aims to enhance conventional mode extraction methods in ocean waveguides using a physics-informed neural network (PINN). Mode extraction involves estimating mode wavenumbers and corresponding mode depth functions. The approach considers a scenario with a single frequency source towed at a constant depth and measured from a vertical line array (VLA). Conventional mode extraction methods applied to experimental data face two problems. First, mode shape estimation is limited because the receivers only cover a partial waveguide. Second, the wavenumber spectrum is affected by issues such
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Satyadharma, Adhika, Heng-Chuan Kan, Ming-Jyh Chern, and Chun-Ying Yu. "Numerical error estimation with physics informed neural network." Computers & Fluids 299 (August 2025): 106700. https://doi.org/10.1016/j.compfluid.2025.106700.

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Tesis sobre el tema "Physics-Informed neural network"

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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.

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In this thesis, we explore Lagrangian Neural Networks (LNNs) for system identification of Autonomous Underwater Vehicles (AUVs) with 6 degrees of freedom. One of the main challenges of AUVs is that they have limited wireless communication and navigation under water. AUVs operate under strict and uncertain conditions, where they need to be able to navigate and perform tasks in unknown ocean environments with limited and noisy sensor data. A crucial requirement for localization and adaptive control of AUVs is having an accurate and reliable model of the system’s nonlinear dynamics while taking i
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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.

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Physics-Informed Neural Networks (PINNs) are hybrid models that incorporate differential equations into the training of neural networks, with the aim of bringing the best of both worlds. This project used a mathematical model describing a Continuous Stirred-Tank Reactor (CSTR), to test two possible applications of PINNs. The first type of PINN was trained to predict an unknown reaction rate law, based only on the differential equation and a time series of the reactor state. The resulting model was used inside a multi-step solver to simulate the system state over time. The results showed that t
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Ding, Simon. "Advancing cosmological field-level inference with physics-informed Bayesian neural networks." Electronic Thesis or Diss., Sorbonne université, 2025. http://www.theses.fr/2025SORUS050.

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La cosmologie repose essentiellement sur des observations passives, obtenues avec des télescopes sophistiqués, pour comprendre l'origine, la dynamique et le destin ultime de l'Univers. Les nouveaux relevés de galaxies propulsent la cosmologie dans une ère dominée par les données, nécessitant des adaptations significatives de nos méthodes d'analyse. L'un des principaux défis de la cosmologie moderne est d'extraire des informations physiques significatives des prochains relevés cosmologiques. Parallèlement à cette révolution, l'apprentissage automatique s'impose comme une technique puissante pou
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Elhawary, Mohamed. "Apprentissage profond informé par la physique pour les écoulements complexes." Electronic Thesis or Diss., Paris, ENSAM, 2024. http://www.theses.fr/2024ENAME068.

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Ce travail de doctorat étudie deux problèmes spécifiques concernant les turbomachines en utilisant des algorithmes d'apprentissage automatique. Le premier se concentre sur un compresseur axial, en abordant les problèmes de décrochage tournant, qui sont des phénomènes instables limitant la plage de fonctionnement des compresseurs. Les avancées récentes comprennent le développement de techniques de contrôle d'écoulement, telles que des jets au niveau du carter et du bord d’attaque du rotor, qui ont montré un potentiel pour étendre les plages de fonctionnement des compresseurs. Cependant, l’optim
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Doumèche, Nathan. "Physics-informed machine learning : a mathematical framework with applications to time series forecasting." Electronic Thesis or Diss., Sorbonne université, 2025. http://www.theses.fr/2025SORUS105.

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L'apprentissage automatique informé par la physique est un domaine récent qui consiste à intégrer des connaissances physiques dans des modèles d'apprentissage automatique. L'information physique prend souvent la forme d'un système d'équations aux dérivées partielles (EDPs) que la fonction de régression doit satisfaire. Dans la première partie de cette thèse, nous analysons les propriétés statistiques des méthodes d'apprentissage automatique informé par la physique. En particulier, nous étudions les propriétés des réseaux de neurones informés par la physique, en termes d'approximation, de consi
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Wu, Dawen. "Solving Some Nonlinear Optimization Problems with Deep Learning." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG083.

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Cette thèse considère quatre types de problèmes d'optimisation non linéaire, à savoir les jeux de bimatrice, les équations de projection non linéaire (NPEs), les problèmes d'optimisation convexe non lisse (NCOPs) et les jeux à contraintes stochastiques (CCGs). Ces quatre classes de problèmes d'optimisation non linéaire trouvent de nombreuses applications dans divers domaines tels que l'ingénierie, l'informatique, l'économie et la finance. Notre objectif est d'introduire des algorithmes basés sur l'apprentissage profond pour calculer efficacement les solutions optimales de ces problèmes d'optim
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Reverdi, Justin. "Vers un capteur virtuel certifiable : évaluation ingenieure, robustesse globale et locale." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2025. http://www.theses.fr/2025TLSEP048.

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Dans les systèmes à cycle de vapeur, le capteur de débit massique joue un rôle clé dans diverses tâches de surveillance et de contrôle. Cependant, les capteurs physiques peuvent être imprécis ou encombrants, et présenter une sensibilité élevée aux vibrations, ce qui est particulièrement problématique lorsque le système est embarqué dans un avion. De plus, ces capteurs peuvent être coûteux et difficiles à entretenir. Pour pallier ces limitations, le développement d’un capteur virtuel basé sur des données issues de capteurs standards apparaît comme une alternative intéressante.Ce manuscrit abord
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Quattromini, Michele. "Graph Neural Networks for fluid mechanics : data-assimilation and optimization." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST161.

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Cette thèse de doctorat explore l'application des réseaux de neurones en graphes (GNN) dans le domaine de la dynamique des fluides numérique (CFD), avec un accent particulier sur l'assimilation de données et l'optimisation. Le travail est structuré en trois parties principales: assimilation de données pour les équations de Navier-Stokes moyennées à la Reynolds (RANS) basée sur des modèles GNN; assimilation de données augmentée par les GNN avec des contraintes physiques imposées par la méthode adjointe; optimisation des systèmes fluides par des techniques d'apprentissage automatique (ML).Dans l
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Doriat, Aurélien. "Caractérisation des couplages aéro-thermo-mécaniques lors d’un vieillissement par thermo-oxydation de composites à matrice polymère soumis à un écoulement rapide et chauffé." Electronic Thesis or Diss., Chasseneuil-du-Poitou, Ecole nationale supérieure de mécanique et d'aérotechnique, 2024. http://www.theses.fr/2024ESMA0018.

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Les matériaux composites à matrice organique renforcés de fibres de carbone (CFRP) sont largement utilisés dans les structures aéronautiques froides. Dans les applications de moteurs aéronautiques, comme les aubes de FAN, ces matériaux peuvent être soumis à des conditions environnementales particulièrement sévères, avec des températures pouvant atteindre 120°C et une vitesse d’écoulement proche de Mach 1.Il est bien établi que les polymères époxy sont sujets à des phénomènes de thermo-oxydation lorsqu’ils sont exposés à des températures élevées. Ce phénomène implique la diffusion et la réactio
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(8828960), Sukirt. "Physics Informed Neural Networks for Engineering Systems." Thesis, 2020.

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<div>This thesis explores the application of deep learning techniques to problems in fluid mechanics, with particular focus on physics informed neural networks. Physics</div><div>informed neural networks leverage the information gathered over centuries in the</div><div>form of physical laws mathematically represented in the form of partial differential</div><div>equations to make up for the dearth of data associated with engineering and physi-</div><div>cal systems. To demonstrate the capability of physics informed neural networks, an</div><div>inverse and a forward problem are considered. The
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Capítulos de libros sobre el tema "Physics-Informed neural network"

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Beniwal, Kirti, and Vivek Kumar. "Gradient-Based Physics-Informed Neural Network." In Third Congress on Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9379-4_54.

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Madenci, Erdogan, Pranesh Roy, and Deepak Behera. "Peridynamics for Physics Informed Neural Network." In Advances in Peridynamics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97858-7_16.

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Li, Yao, Yuanxun Xu, Shengzhu Shi, and Boying Wu. "Adversarial Adaptive Sampling for Physics-Informed Neural Network." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-77688-5_41.

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Ho, Ya-Chi, Chia-Lin Chang, Tai-Te Lee, and Yu-Hui Huang. "Physics-Informed Neural Network for Shock Absorber Design." In Communications in Computer and Information Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4596-1_19.

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Naveen Raj, R. "Physics Informed Neural Network for Solution of Duffing Oscillators." In Springer Proceedings in Physics. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-69146-1_14.

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Dhamirah Mohamad, Najwa Zawani, Akram Yousif, Nasiha Athira Binti Shaari, et al. "Heat Transfer Modelling with Physics-Informed Neural Network (PINN)." In Studies in Systems, Decision and Control. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04028-3_3.

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Mahesh, Ragini Bal, Jorge Leandro, and Qing Lin. "Physics Informed Neural Network for Spatial-Temporal Flood Forecasting." In Lecture Notes in Civil Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5501-2_7.

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Oh, Dong Keun. "Pure Physics-Informed Echo State Network of ODE Solution Replicator." In Artificial Neural Networks and Machine Learning – ICANN 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44201-8_19.

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Wu, Mingyu, Yafei Wang, Yichen Zhang, and Zexing Li. "Physics-Informed Neural Network for Mining Truck Suspension Parameters Identification." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_94.

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AbstractMining truck suspensions are prone to performance degradation under complex external excitation of the mining area, leading to high safety risks and maintenance costs. However, the lack of unsprung kinematic information and harsh operating environments lead to inadequate accuracy of current physical models. On the other hand, data-driven methods partially address the issue of incomplete information, but suffer from the absence of interpretability and generalization. To address these challenges, this paper introduces a Physics-Informed Neural Network (PINN) for precise suspension charac
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Ibrahim, Abdul Qadir, Sebastian Götschel, and Daniel Ruprecht. "Parareal with a Physics-Informed Neural Network as Coarse Propagator." In Euro-Par 2023: Parallel Processing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39698-4_44.

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AbstractParallel-in-time algorithms provide an additional layer of concurrency for the numerical integration of models based on time-dependent differential equations. Methods like Parareal, which parallelize across multiple time steps, rely on a computationally cheap and coarse integrator to propagate information forward in time, while a parallelizable expensive fine propagator provides accuracy. Typically, the coarse method is a numerical integrator using lower resolution, reduced order or a simplified model. Our paper proposes to use a physics-informed neural network (PINN) instead. We demon
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Actas de conferencias sobre el tema "Physics-Informed neural network"

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Miao, Yuyang, Haolin Li, and Danilo Mandic. "GPINN: Physics-Informed Neural Network with Graph Embedding." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651053.

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Jahanbakhsh, Amirmohammad, Rojan Firouznia, Sina Nazifi, and Hadi Ghasemi. "Physics-Informed Neural Network on Thin Film Evaporation." In 2024 23rd IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm). IEEE, 2024. http://dx.doi.org/10.1109/itherm55375.2024.10709585.

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Bakirtzis, Stefanos, Marco Fiore, and Ian Wassell. "Towards Physics-Informed Graph Neural Network-based Computational Electromagnetics." In 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI). IEEE, 2024. http://dx.doi.org/10.1109/ap-s/inc-usnc-ursi52054.2024.10686000.

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Glosemeyer, Tom, Julian Lich, Robert Kuschmierz, and Juergen Czarske. "3D Diffuser Encoded Imaging and Physics-Informed Neural Network Reconstruction." In Frontiers in Optics. Optica Publishing Group, 2024. https://doi.org/10.1364/fio.2024.fw6d.1.

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Minimally invasive fiber endoscopy offers a high potential for biomedical imaging applications. By utilizing a diffuser for encoding and a coherent fiber bundle in conjunction with neural networks for reconstruction, single-shot 3D imaging enabled. Full-text article not available; see video presentation
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Serrano, Gil, Marcelo Jacinto, José Ribeiro-Gomes, et al. "Physics-Informed Neural Network for Multirotor Slung Load Systems Modeling." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610582.

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Zhao, Michael Yong, Olzhas Mukhmetov, Aigerim Mashekova, et al. "Application of Physics Informed Neural Network for Breast Cancer Detection." In 2024 9th International Conference on Automation, Control and Robotics Engineering (CACRE). IEEE, 2024. http://dx.doi.org/10.1109/cacre62362.2024.10635033.

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Eeckhout, Victor, Hossein Fani, Md Umar Hashmi, and Geert Deconinck. "Improved Physics-Informed Neural Network based AC Power Flow for Distribution Networks." In 2024 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE). IEEE, 2024. https://doi.org/10.1109/isgteurope62998.2024.10863674.

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Wang, Ziwen, Qimin Wang, Weisheng Zhang, Sheng Zhang, Shanmei Chen, and Chao Li. "Enhanced Physics-Informed Neural Network with Coarse Mesh Finite Element Pretraining." In 2025 5th International Conference on Neural Networks, Information and Communication Engineering (NNICE). IEEE, 2025. https://doi.org/10.1109/nnice64954.2025.11063871.

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Chen, Likun, Xuzhu Dong, Yifan Wang, Wei Sun, Bo Wang, and Gareth Harrison. "Physics-Informed Neural Network for Microgrid Forward/Inverse Ordinary Differential Equations." In 2024 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2024. http://dx.doi.org/10.1109/pesgm51994.2024.10688678.

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Zhang, Qian, Yuan Sui, Stefan Rothe, and Jürgen W. Czarske. "Learning to decompose multimode fibers using a physics-informed neural network." In Emerging Topics in Artificial Intelligence (ETAI) 2024, edited by Giovanni Volpe, Joana B. Pereira, Daniel Brunner, and Aydogan Ozcan. SPIE, 2024. http://dx.doi.org/10.1117/12.3027588.

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Informes sobre el tema "Physics-Informed neural network"

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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), 2023. http://dx.doi.org/10.2172/2008057.

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Pettit, Chris, and D. Wilson. A physics-informed neural network for sound propagation in the atmospheric boundary layer. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41034.

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We describe what we believe is the first effort to develop a physics-informed neural network (PINN) to predict sound propagation through the atmospheric boundary layer. PINN is a recent innovation in the application of deep learning to simulate physics. The motivation is to combine the strengths of data-driven models and physics models, thereby producing a regularized surrogate model using less data than a purely data-driven model. In a PINN, the data-driven loss function is augmented with penalty terms for deviations from the underlying physics, e.g., a governing equation or a boundary condit
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Keidar, Michael, and Li Lin. Generative Physics-Informed Neural Network Solving Multi-Scale and Multi-Phase Plasma Chemical Flow Field. Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2478929.

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Wells, Daniel, Benjamin Baker, and Kristine Pankow. The Feasibility of Incorporating a 3D Velocity Model Into Earthquake Location Around Salt Lake City, UT Using a Physics Informed Neural Network. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2430497.

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Nadiga, Balasubramanya, and Robert Lowrie. Physics Informed Neural Networks as Computational Physics Emulators. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1985825.

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Guan, Jiajing, Sophia Bragdon, and Jay Clausen. Predicting soil moisture content using Physics-Informed Neural Networks (PINNs). Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48794.

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Environmental conditions such as the near-surface soil moisture content are valuable information in object detection problems. However, such information is generally unobtainable at the necessary scale without active sensing. Richards’ equation is a partial differential equation (PDE) that describes the infiltration process of unsaturated soil. Solving the Richards’ equation yields information about the volumetric soil moisture content, hydraulic conductivity, and capillary pressure head. However, Richards’ equation is difficult to approximate due to its nonlinearity. Numerical solvers such as
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D'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), 2020. http://dx.doi.org/10.2172/1614899.

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Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maint
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Mosalam, Khalid, Issac Pang, and Selim Gunay. Towards Deep Learning-Based Structural Response Prediction and Ground Motion Reconstruction. Pacific Earthquake Engineering Research Center, 2025. https://doi.org/10.55461/ipos1888.

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This research presents a novel methodology that uses Temporal Convolutional Networks (TCNs), a state-of-the-art deep learning architecture, for predicting the time history of structural responses to seismic events. By leveraging accelerometer data from instrumented buildings, the proposed approach complements traditional structural analysis models, offering a computationally efficient alternative to nonlinear time history analysis. The methodology is validated across a broad spectrum of structural scenarios, including buildings with pronounced higher-mode effects and those exhibiting both line
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SECOND-ORDER ANALYSIS OF BEAM-COLUMNS BY MACHINE LEARNING-BASED STRUCTURAL ANALYSIS THROUGH PHYSICS-INFORMED NEURAL NETWORKS. The Hong Kong Institute of Steel Construction, 2023. http://dx.doi.org/10.18057/ijasc.2023.19.4.10.

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The second-order analysis of slender steel members could be challenging, especially when large deflection is involved. This paper proposes a novel machine learning-based structural analysis (MLSA) method for second-order analysis of beam-columns, which could be a promising alternative to the prevailing solutions using over-simplified analytical equations or traditional finite-element-based methods. The effectiveness of the conventional machine learning method heavily depends on both the qualitative and the quantitative of the provided data. However, such data are typically scarce and expensive
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