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Relevant bibliographies by topics / PINNs

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Academic literature on the topic 'PINNs'

Author: Grafiati

Published: 30 June 2021

Last updated: 29 July 2025

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Journal articles on the topic "PINNs"

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Khalid, Salman, Muhammad Haris Yazdani, Muhammad Muzammil Azad, Muhammad Umar Elahi, Izaz Raouf, and Heung Soo Kim. "Advancements in Physics-Informed Neural Networks for Laminated Composites: A Comprehensive Review." Mathematics 13, no. 1 (2024): 17. https://doi.org/10.3390/math13010017.

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Physics-Informed Neural Networks (PINNs) integrate physics principles with machine learning, offering innovative solutions for complex modeling challenges. Laminated composites, characterized by their anisotropic behavior, multi-layered structures, and intricate interlayer interactions, pose significant challenges for traditional computational methods. PINNs address these issues by embedding governing physical laws directly into neural network architectures, enabling efficient and accurate modeling. This review provides a comprehensive overview of PINNs applied to laminated composites, highlig

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Faroughi, Salah A., Ramin Soltanmohammadi, Pingki Datta, Seyed Kourosh Mahjour, and Shirko Faroughi. "Physics-Informed Neural Networks with Periodic Activation Functions for Solute Transport in Heterogeneous Porous Media." Mathematics 12, no. 1 (2023): 63. http://dx.doi.org/10.3390/math12010063.

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Simulating solute transport in heterogeneous porous media poses computational challenges due to the high-resolution meshing required for traditional solvers. To overcome these challenges, this study explores a mesh-free method based on deep learning to accelerate solute transport simulation. We employ Physics-informed Neural Networks (PiNN) with a periodic activation function to solve solute transport problems in both homogeneous and heterogeneous porous media governed by the advection-dispersion equation. Unlike traditional neural networks that rely on large training datasets, PiNNs use stron

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Kim, Jaeseung, and Hwijae Son. "Causality-Aware Training of Physics-Informed Neural Networks for Solving Inverse Problems." Mathematics 13, no. 7 (2025): 1057. https://doi.org/10.3390/math13071057.

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Inverse Physics-Informed Neural Networks (inverse PINNs) offer a robust framework for solving inverse problems governed by partial differential equations (PDEs), particularly in scenarios with limited or noisy data. However, conventional inverse PINNs do not explicitly incorporate causality, which hinders their ability to capture the sequential dependencies inherent in physical systems. This study introduces Causal Inverse PINNs (CI-PINNs), a novel framework that integrates directional causality constraints across both temporal and spatial domains. Our approach leverages customized loss functi

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Feng, Zhi-Ying, Xiang-Hua Meng, and Xiao-Ge Xu. "The data-driven localized wave solutions of KdV-type equations via physics-informed neural networks with a priori information." AIMS Mathematics 9, no. 11 (2024): 33263–85. http://dx.doi.org/10.3934/math.20241587.

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<p>In the application of physics-informed neural networks (PINNs) for solutions of partial differential equations, the optimizer may fall into a bad local optimal solution during the training of the network. In this case, the shape of the desired solution may deviate from that of the real solution. To address this problem, we have combined the priori information and knowledge transfer with PINNs. The physics-informed neural networks with a priori information (pr-PINNs) were introduced here, which allow the optimizer to converge to a better solution, improve the training accuracy, and red

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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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Chen, Yanlai, Yajie Ji, Akil Narayan, and Zhenli Xu. "TGPT-PINN: Nonlinear model reduction with transformed GPT-PINNs." Computer Methods in Applied Mechanics and Engineering 430 (October 2024): 117198. http://dx.doi.org/10.1016/j.cma.2024.117198.

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Ma, Shaojuan, Baolan Li, Hufei Li, and Hui Xiao. "PINNs Method for Sloving the Probability Response of the Stochastic Linear System with Fractional Gaussian Noise." Journal of Physics: Conference Series 3004, no. 1 (2025): 012016. https://doi.org/10.1088/1742-6596/3004/1/012016.

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Abstract In this paper, the Physics-informed neural networks (PINNs) algorithm is mainly used to solve the time-dependent Fokker-Planck-Kolmogorov (FPK) equation, and eventually obtain the transient probability density function (PDF). Firstly, we derive the FPK equation for a dynamical system driven by multiplicative fractional Gaussian noise (fGn). Secondly, a deep learning method based on PINNs is introduced for resolving the corresponding time-dependent FPK equation. Finally, one examples under fGn excitation condition is discussed to determine the effectiveness and feasibility of PINNs alg

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Ko, Taehwan, Heuisu Kim, Yeoungcheol Shin, et al. "Review of Recent Additive Manufacturing and Welding Research with Application of Physics-Informed Neural Networks." Journal of Welding and Joining 42, no. 4 (2024): 357–65. http://dx.doi.org/10.5781/jwj.2024.42.4.3.

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This review introduces recent research on applying physics-informed neural networks (PINNs) to additive manufacturing and welding. PINNs, which are artificial intelligence models, integrate governing equations containing physical information with artificial neural networks, enabling the modeling of complex physical phenomena at a lower computational cost than traditional numerical models. Although PINNs have been employed in a limited number of studies on welding processes, they have been extensively used in various studies within the field of additive manufacturing. This study reviews the the

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Demir, Kubilay Timur, Kai Logemann, and David S. Greenberg. "Closed-Boundary Reflections of Shallow Water Waves as an Open Challenge for Physics-Informed Neural Networks." Mathematics 12, no. 21 (2024): 3315. http://dx.doi.org/10.3390/math12213315.

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Physics-informed neural networks (PINNs) have recently emerged as a promising alternative to traditional numerical methods for solving partial differential equations (PDEs) in fluid dynamics. By using PDE-derived loss functions and auto-differentiation, PINNs can recover solutions without requiring costly simulation data, spatial gridding, or time discretization. However, PINNs often exhibit slow or incomplete convergence, depending on the architecture, optimization algorithms, and complexity of the PDEs. To address these difficulties, a variety of novel and repurposed techniques have been int

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Roh, Dong Min, Minxue He, Zhaojun Bai, et al. "Physics-Informed Neural Networks-Based Salinity Modeling in the Sacramento–San Joaquin Delta of California." Water 15, no. 13 (2023): 2320. http://dx.doi.org/10.3390/w15132320.

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Salinity in estuarine environments has been traditionally simulated using process-based models. More recently, data-driven models including artificial neural networks (ANNs) have been developed for simulating salinity. Compared to process-based models, ANNs yield faster salinity simulations with comparable accuracy. However, ANNs are often purely data-driven and not constrained by physical laws, making it difficult to interpret the causality between input and output data. Physics-informed neural networks (PINNs) are emerging machine-learning models to integrate the benefits of both process-bas

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Dissertations / Theses on the topic "PINNs"

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Gear, Annabel Jane. "Holocene vegetation history and the palaeoecology of Pinns sylvestris in north Scotland." Thesis, Durham University, 1989. http://etheses.dur.ac.uk/1020/.

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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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Santini, Filippo. "Towards the characterisation of adaptive syndromes of Mediterranean pines: insights through innovative tree phenotyping techniques." Doctoral thesis, Universitat de Lleida, 2020. http://hdl.handle.net/10803/668898.

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En aquesta tesi, es van estudiar patrons de variació genètica intraespecífica en caracters funcionals de Pinus halepensis Mill., Pinus nigra Arnold i Pinus sylvestris L. Un conjunt de tècniques de fenotipat ben establertes van ser combinades amb dades de teledetecció obtingudes amb un dron (unmanned aerial vehicle, UAV). Les imatges d’UAV van indicar una diferenciació genètica en estratègies adaptatives de P. halepensis, determinada per la variació en la disponibilitat d'aigua dins la distribució geogràfica de l’espècie. En P. nigra, la diferenciació genètica va ser determinada per les te

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Mathur, G. "Morphogenetic studies of two Indian pines in vitro: pinus roxburghii sarg and pinus wallichiana A. B. jacks." Thesis(Ph.D.), CSIR-National Chemical Laboratory, Pune, 2001. http://dspace.ncl.res.in:8080/xmlui/handle/20.500.12252/2300.

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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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Kuo, Eva Grotkopp. "Genome size evolution, seedling relative growth rate, and invasiveness in pines (Pinus) /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2003. http://uclibs.org/PID/11984.

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Rautio, Pasi. "Nutrient alterations in Scots pines (Pinus sylvestris L.) under sulphur and heavy metal pollution /." Oulu : Oulun Yliopisto, 2000. http://herkules.oulu.fi/isbn9514257839.

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Lara, S. F. "Taxonomy and genetic variation of pinyon pines (Pinus subsection Cemroides) in the northeast of Mexico." Thesis, University of Edinburgh, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.653683.

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The group <i>Pinus </i>subsection <i>Cembroides </i>belongs to section <i>Parrya </i>which, together with section <i>Strobus, </i>forms subgenus <i>Strobus, </i>the haploxylon or “soft pines”. There are 14 taxa recognized for Mexico. Eight <i>(Pinus catarinae, P. cembroides, P. culminicola, P. johannis, P. nelsonii, P. pinceana, P. remota </i>and <i>P. cembroides </i>var. <i>bicolor) </i>occur in the Northeast. Two of these species, P. <i>nelsonii </i>and <i>P. pinceana </i>are highly localized and are clearly defined taxonomically on the basis of cone morphology, whereas the other species are

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Favela, Lara Susana. "Taxonomy and genetic variation of pinyon pines (Pinus subsection Cembroides) in the northeast of Mexico." Thesis, University of Edinburgh, 2004. http://hdl.handle.net/1842/14830.

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Clavijo, Érika Manuela Asteria 1986. "Avaliação in vitro da resistência à fratura de raízes dentais fragilizadas reabilitadas com núcleos de preenchimento associados a pinos de fibra de vidro." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/290471.

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Orientador: Caio Cezar Randi Ferraz<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Odontologia de Piracicaba<br>Made available in DSpace on 2018-08-22T04:14:56Z (GMT). No. of bitstreams: 1 Clavijo_ErikaManuelaAsteria_M.pdf: 12544846 bytes, checksum: e5ccc90481f71cac564a4038b3f3cc5f (MD5) Previous issue date: 2013<br>Resumo: Este trabalho avaliou in vitro a resistência à fratura de raízes de incisivos bovinos com 3 espessuras de dentina, nas quais, foram cimentados pinos de fibra de vidro com e sem reembasamento com resina composta. 180 raízes bovinas foram padron

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Books on the topic "PINNs"

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Galiano, Javier Peña. Casa de los Pinos: Murcia/ Spain = house among the Pines. Irreversible, 2008.

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1950-, Pae Pyŏng-u., Pae Pyŏng-u. 1950-, Fabre, Thierry, writer of supplementary textual content, and Tassone, Éric, writer of supplementary textual content, eds. Et les pins brodaient le ciel: And pines embroidered the sky. Arnaud Bizalion éditeur, 2018.

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Roots, Hilary. Ile des pins ... un Eden austral = Isle of pines ... an austral Eden. Editions Solaris, 1999.

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Masahiko, Kimura, Yosiaki Takashita, Schoech Wayne, and Persiano Michael, eds. Pines. Stone Lantern Pub., 2005.

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Todd, Christine. Pins. Red Anemone Books, 2010.

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Dunbar, Robert. The pines. Leisure, 2008.

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Ginta, Gerharde-Upeniece, and Valsts Mākslas muzejs (Latvia), eds. Rūdolfs Pinnis. Valsts Mākslas muzejs, 2002.

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Doyle, Roddy. Two pints. Chivers, 2013.

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Sutiara, Nia. Pondok pinus. Gramedia Widiasarana Indonesia, 2003.

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Rosenzweig, Emmanuelle. Les pin's. Syros/Alternatives, 1991.

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Book chapters on the topic "PINNs"

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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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Cuomo, Salvatore, Donato Cerciello, Francesco Piccialli, and Vincenzo Vocca. "Solving Soil Microbiota Growth Problem by PINNs." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85700-3_18.

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Mallik, Ekansh, and Navnit Jha. "PINNs for Solving Fractal Order Differential Equation." In Innovations in Sustainable Technologies and Computing. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-6794-6_38.

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Humphrey, Brian E. "Pinus (Pinaceae) – Pines." In The Bench Grafter’s Handbook. CRC Press, 2019. http://dx.doi.org/10.1201/9781315171463-46.

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Florido, Jose, He Wang, Amirul Khan, and Peter K. Jimack. "Investigating Guiding Information for Adaptive Collocation Point Sampling in PINNs." In Computational Science – ICCS 2024. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63759-9_36.

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Sun, Yuqiu, and Wei Xv. "Application of Physical Information Neural Network Based on Fourier Features in Electromagnetic Computing." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4856-6_6.

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Abstract In recent years, artificial intelligence technologies have been widely applied across various fields, demonstrating significant potential, particularly in scientific computing. This paper proposes a neural network approach based on Fourier features to solve partial differential equations (PDEs) related to electromagnetic physical laws, starting from the general solution form of PDEs. By integrating Physics-Informed Neural Networks (PINNs) with automatic differentiation techniques, a loss function is constructed based on PDEs and known conditions. The effectiveness and accuracy of this

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Ehebrecht, Frank, Toni Scharle, and Martin Atzmueller. "Constructing Neural Forms for Hard-Constraint PINNs with Complex Dirichlet Boundaries." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-74633-8_37.

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Barboza, Gabriel, and Isaac P. dos Santos. "Application of the DeepXDE Library for Solving Mathematical Models with PINNs." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-97596-7_8.

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Sahay, Prasant, Krishna Kant Mishra, Aryan Verma, Rajnish Mallick, Dineshkumar Harursampath, and Manoj Sahni. "Comprehensive Analysis of Damping Conditions Using PINNs with Different Activation Functions and Optimizers." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1449-3_48.

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Girault, Benjamin, Rémi Emonet, Amaury Habrard, Jordan Patracone, and Marc Sebban. "Approximation Error of Sobolev Regular Functions with Tanh Neural Networks: Theoretical Impact on PINNs." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70359-1_16.

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Conference papers on the topic "PINNs"

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Deng, Qinyu, Huan Tang, Renxian Li, et al. "Application of PINNs in PNJ Research." In 2024 Photonics & Electromagnetics Research Symposium (PIERS). IEEE, 2024. http://dx.doi.org/10.1109/piers62282.2024.10617967.

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Moreno-Palancas, Isabela Fons, Raquel Salcedo D�az, Rub�n Ruiz Femenia, and Jos� A. Caballero. "Optimal Design and Control of Chemical Reactors using PINN-based frameworks." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.140730.

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In an era defined by economic competitiveness and environmental awareness, engineering solutions must maximize profitability, efficiency and sustainability, underscoring the relevance of process optimization and the societal impact any contribution in this research field would bring. In chemical reactor engineering, optimization tasks pose significant challenges due to the highly non-linear and non-convex nature of reactor models, often involving differential equations. While conventional approaches have proven to be reliable strategies for solving these complex problems, their application bec

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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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Cao, Yuandong, Chi Chiu So, Junmin Wang, and Siu Pang Yung. "System Stabilization of PDEs using Physics-Informed Neural Networks (PINNs)." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662626.

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Liu, Lin, and Yuan Yuan. "Effective Training of PINNs by Combining CMA-ES with Gradient Descent." In 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024. http://dx.doi.org/10.1109/cec60901.2024.10611964.

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Mishra, Sourav, Shreya Hallikeri, and Suresh Sundaram. "REAct: Rational Exponential Activation for Better Learning and Generalization in PINNs." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10887788.

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Alkhadhr, Shaikhah, and Mohamed Almekkawy. "Multifrequency Encoding in PINNs for Precision Wave Equation Modeling in Inhomogeneous Media." In 2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS). IEEE, 2024. https://doi.org/10.1109/uffc-js60046.2024.10793950.

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Fernandes, Thiago E., Rodrigo W. Dos Santos, and Marcelo Lobosco. "Bridging Accuracy and Efficiency: The Role of PINNs in Immune Response Simulations." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822444.

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Gokulavani, D., and J. Mohana. "Enhancing Pupil Detection Accuracy for Medical Diagnostics using PINNs Combined with VGG19." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10961702.

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Li, Wei, Huan Tang, Renxian Li, et al. "Electromagnetic Scattering of Infinitely Long Cylinder of Arbitrary Cross-section Based on PINNs." In 2024 Photonics & Electromagnetics Research Symposium (PIERS). IEEE, 2024. http://dx.doi.org/10.1109/piers62282.2024.10618183.

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Reports on the topic "PINNs"

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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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Wenting, Li, Wang Ren, and Ignacio Cominges. A Comprehensive Analysis of PINNs for Power System Transient Stability. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2375841.

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POLLACK, Katharina, Florian PAUSCH, and Piotr MAJDAK. Parametric pinna model for a realistic representation of listener-specific pinna geometry. Verlag der Österreichischen Akademie der Wissenschaften, 2025. https://doi.org/10.1553/ica_2022_parametric-model.

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Personalised binaural audio requires individual head-related transfer functions (HRTFs). Nowadays, it is feasible to compute HRTFs numerically from meshes of a listener’s anatomy. These meshes require geometric details of the pinna which are difficult to capture and often corrupted by noise and outliers. The alignment of a highresolution template geometry to the captured listener geometry seems to be a promising approach to deal with these corruptions. However, such an alignment represents a multi-dimensional optimisation problem. To tackle this, we describe a parametric pinna model which redu

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E.H. Seabury. PINS Variance study. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1107265.

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E.H. Seabury. PINS-3X Operations. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1111014.

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Robertson, Marvin D. Ported-Coax Interior Sensor (PINTS). Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada198248.

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A.J. Caffrey. PINS Spectrum Identification Guide. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1070121.

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Arno, Stephen F., and Raymond J. Hoff. Silvics of whitebark pine (Pinus albicaulis). U.S. Department of Agriculture, Forest Service, Intermountain Research Station, 1989. http://dx.doi.org/10.2737/int-gtr-253.

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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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Echt, Craig, and Sedley Josserand. DNA fingerprinting sets for four southern pines. U.S. Department of Agriculture, Forest Service, Southern Research Station, 2018. http://dx.doi.org/10.2737/srs-rn-24.

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