Relevant bibliographies by topics / Physics Informed Neural Networks

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Physics Informed Neural Networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Physics Informed Neural Networks"

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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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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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Pang, Guofei, Lu Lu, and George Em Karniadakis. "fPINNs: Fractional Physics-Informed Neural Networks." SIAM Journal on Scientific Computing 41, no. 4 (2019): A2603—A2626. http://dx.doi.org/10.1137/18m1229845.

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Song, Yanjie, He Wang, He Yang, Maria Luisa Taccari, and Xiaohui Chen. "Loss-attentional physics-informed neural networks." Journal of Computational Physics 501 (March 2024): 112781. http://dx.doi.org/10.1016/j.jcp.2024.112781.

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Rojas, Sergio, Paweł Maczuga, Judit Muñoz-Matute, David Pardo, and Maciej Paszyński. "Robust Variational Physics-Informed Neural Networks." Computer Methods in Applied Mechanics and Engineering 425 (May 2024): 116904. http://dx.doi.org/10.1016/j.cma.2024.116904.

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Henkes, Alexander, Henning Wessels, and Rolf Mahnken. "Physics informed neural networks for continuum micromechanics." Computer Methods in Applied Mechanics and Engineering 393 (April 2022): 114790. http://dx.doi.org/10.1016/j.cma.2022.114790.

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Chen, Haotian, Enno Kätelhön, and Richard G. Compton. "Predicting Voltammetry Using Physics-Informed Neural Networks." Journal of Physical Chemistry Letters 13, no. 2 (2022): 536–43. http://dx.doi.org/10.1021/acs.jpclett.1c04054.

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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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Lee, Sang-Min. "Physics-Informed Neural Networks and its Applications." Journal of the Korea Academia-Industrial cooperation Society 23, no. 12 (2022): 755–60. http://dx.doi.org/10.5762/kais.2022.23.12.755.

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Son, Hwijae, Jin Woo Jang, Woo Jin Han, and Hyung Ju Hwang. "Sobolev training for physics-informed neural networks." Communications in Mathematical Sciences 21, no. 6 (2023): 1679–705. http://dx.doi.org/10.4310/cms.2023.v21.n6.a11.

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Dissertations / Theses on the topic "Physics Informed Neural Networks"

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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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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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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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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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Jing, Li Ph D. Massachusetts Institute of Technology. "Physical symmetry enhanced neural networks." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/128294.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, February, 2020<br>Cataloged from student-submitted PDF version of thesis<br>Includes bibliographical references (pages 91-99).<br>Artificial Intelligence (AI), widely considered "the fourth industrial revolution", has shown its potential to fundamentally change our world. Today's AI technique relies on neural networks. In this thesis, we propose several physical symmetry
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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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Sutherland, Connie. "Spatio-temporal feedback in stochastic neural networks." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27559.

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The mechanisms by which groups of neurons interact is an important facet to understanding how the brain functions. Here we study stochastic neural networks with delayed feedback. The first part of our study looks at how feedback and noise affect the mean firing rate of the network. Secondly we look at how the spatial profile of the feedback affects the behavior of the network. Our numerical and theoretical results show that negative (inhibitory) feedback linearizes the frequency vs input current (f-I) curve via the divisive gain effect it has on the network. The interaction of the inhibitory f
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Squadrani, Lorenzo. "Deep neural networks and thermodynamics." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Deep learning is the most effective and used approach to artificial intelligence, and yet it is far from being properly understood. The understanding of it is the way to go to further improve its effectiveness and in the best case to gain some understanding of the "natural" intelligence. We attempt a step in this direction with the aim of physics. We describe a convolutional neural network for image classification (trained on CIFAR-10) within the descriptive framework of Thermodynamics. In particular we define and study the temperature of each component of the network. Our results provides a n
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Gyawali, Gaurav. "Solving Atomic Wave Functions Using Artificial Neural Networks." ScholarWorks@UNO, 2018. https://scholarworks.uno.edu/honors_theses/104.

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Carleo and Troyer [3] have recently pointed out the possibility of solving quantum many-body problems by using Artificial Neural Networks (ANN). Their work is based on minimizing a variational wave function to obtain the ground states for various spin-dependent systems. This work is primarily focused on developing efficient method using ANN to solve the ground state wave function for atomic systems. We have developed a theoretical groundwork to represent the wave function of a many-electron atom by using artificial neural network while still preserving its antisymmetric property. By using the
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Books on the topic "Physics Informed Neural Networks"

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Aubin, Jean Pierre. Neural networks and qualitative physics. Cambridge University Press, 1996.

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Aubin, Jean Pierre. Neural networks and qualitative physics. Cambridge University Press, 2011.

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E, Domany, and Cowan J, eds. Models of Neural Networks IV.: The Visual System - Physics of Neural Networks. Springer-Verlag New York, Incorporated, 2002.

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Murray, Alan F. Applications of Neural Networks. Springer US, 1995.

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Zhang, Xiang-Sun. Neural Networks in Optimization. Springer US, 2000.

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E, Golès, Martínez Servet, and School on Statistical Physics and Cooperative Systems (2nd : 1990 : Santiago, Chile), eds. Statistical physics, automata networks, and dynamical systems. Kluwer Academic Publishers, 1992.

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Pham, Duc Truong. Neural Networks for Identification, Prediction and Control. Springer London, 1995.

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Akhmet, Marat, and Mehmet Onur Fen. Replication of Chaos in Neural Networks, Economics and Physics. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-47500-3.

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Delgado-Frias, José G. VLSI for Artificial Intelligence and Neural Networks. Springer US, 1991.

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Workshop on Neural Networks: from Biology to High Energy Physics (2nd 1992 Isola d'Elba, Italy). Second Workshop on Neural Networks: from Biology to High Energy Physics, Isola d'Elba, Italy, June 18-26, 1992. Edited by Benhar Omar. World Scientific, 1993.

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Book chapters on the topic "Physics Informed Neural Networks"

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Kollmannsberger, Stefan, Davide D’Angella, Moritz Jokeit, and Leon Herrmann. "Physics-Informed Neural Networks." In Deep Learning in Computational Mechanics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76587-3_5.

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Awojoyogbe, Bamidele O., and Michael O. Dada. "Physics Informed Neural Networks (PINNs)." In Series in BioEngineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-6370-2_2.

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Goswami, Somdatta, Aniruddha Bora, Yue Yu, and George Em Karniadakis. "Physics-Informed Deep Neural Operator Networks." In Computational Methods in Engineering & the Sciences. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36644-4_6.

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Anitescu, Cosmin, Burak İsmail Ateş, and Timon Rabczuk. "Physics-Informed Neural Networks: Theory and Applications." In Computational Methods in Engineering & the Sciences. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36644-4_5.

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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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Turinici, Gabriel. "Optimal Time Sampling in Physics-Informed Neural Networks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-78395-1_15.

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Vemuri, Sai Karthikeya, Tim Büchner, Julia Niebling, and Joachim Denzler. "Functional Tensor Decompositions for Physics-Informed Neural Networks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-78389-0_3.

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Johnson, Rob, Soukaïna Filali Boubrahimi, Omar Bahri, and Shah Muhammad Hamdi. "Physics-Informed Neural Networks for Solar Wind Prediction." In Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37731-0_21.

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de Wolff, Taco, Hugo Carrillo, Luis Martí, and Nayat Sanchez-Pi. "Optimal Architecture Discovery for Physics-Informed Neural Networks." In Advances in Artificial Intelligence – IBERAMIA 2022. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-22419-5_7.

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Kim, Hyea Hyun, and Hee Jun Yang. "Domain Decomposition Algorithms for Physics-Informed Neural Networks." In Domain Decomposition Methods in Science and Engineering XXVI. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95025-5_76.

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Conference papers on the topic "Physics Informed Neural Networks"

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Zhu, Qing, Yucong Shi, Yun Feng, and Yaonan Wang. "Physics-Informed Neural Networks for RUL Prediction." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865662.

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Mahmud, Istiak, Ayush Asthana, Mark Hoffmann, and Ahmeb Abdelhadi. "Physics-Informed Neural Networks for Quantum Wavefunctions." In 2024 International Conference on Computer and Applications (ICCA). IEEE, 2024. https://doi.org/10.1109/icca62237.2024.10927810.

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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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Das, Indrajit, Debjit Das, Papiya Debnath, Subhrapratim Nath, and Manash Chanda. "Physics Informed Neural Networks(PINNs) for Burgers' equation." In 2024 4th International Conference on Artificial Intelligence and Signal Processing (AISP). IEEE, 2024. https://doi.org/10.1109/aisp61711.2024.10870712.

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Lv, Siyuan, Qianxi Cheng, Haojie Gong, Hao Gao, Dong Zhou, and Zheng Duanmu. "Scientific Physics-Informed Neural Networks on Silicon Membranes." In 2024 4th International Conference on Electronic Information Engineering and Computer (EIECT). IEEE, 2024. https://doi.org/10.1109/eiect64462.2024.10866121.

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Zhang, Xin, Nanxi Chen, Jiyan Qiu, Pengcheng Shi, Xuesong Wu, and Wu Yuan. "Importance-Guided Sequential Training for Physics-Informed Neural Networks." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651329.

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Yokota, Kazuya, Masataka Ogura, Takahiko Kurahashi, and Masajiro Abe. "Physics-Informed CNN for the Design of Acoustic Equipment." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650136.

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Das, Indrajit, Debjit Das, Abheepsa Bhattacharya, Papiya Debnath, Subhrapratim Nath, and Manash Chanda. "Time- Dependent Eikonal Solution Using Physics-Informed Neural Networks." In 2024 IEEE International Conference of Electron Devices Society Kolkata Chapter (EDKCON). IEEE, 2024. https://doi.org/10.1109/edkcon62339.2024.10870849.

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Zendehdel, Niloofar, Adib Mosharrof, Katherine Delgado, Daoru Han, Xin Liang, and Tong Shu. "Modeling Lunar Surface Charging Using Physics-Informed Neural Networks." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825168.

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Tosun, Rıza Arman, Deniz Kuzucu, Ahmet Cemal Durgun, and Mustafa Gökçe Baydoğan. "Fine-Pitch Interconnect Modeling Using Physics-Informed Neural Networks." In 2025 IEEE 29th Workshop on Signal and Power Integrity (SPI). IEEE, 2025. https://doi.org/10.1109/spi64682.2025.11014453.

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Reports on the topic "Physics Informed Neural Networks"

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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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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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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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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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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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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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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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Pasupuleti, Murali Krishna. Phase Transitions in High-Dimensional Learning: Understanding the Scaling Limits of Efficient Algorithms. National Education Services, 2025. https://doi.org/10.62311/nesx/rr1125.

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Abstract: High-dimensional learning models exhibit phase transitions, where small changes in model complexity, data size, or optimization dynamics lead to abrupt shifts in generalization, efficiency, and computational feasibility. Understanding these transitions is crucial for scaling modern machine learning algorithms and identifying critical thresholds in optimization and generalization performance. This research explores the role of high-dimensional probability, random matrix theory, and statistical physics in analyzing phase transitions in neural networks, kernel methods, and convex vs. no
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