المؤلفون: Ghalambaz, Mohammad, Sheremet, Mikhail A., Khan, Mohammed Arshad, Raizah, Zehba, Shafi, Jana

المصدر: International Journal of Numerical Methods for Heat & Fluid Flow, 2024, Vol. 34, Issue 8, pp. 3131-3165.

URL الوصول: http://www.emeraldinsight.com/doi/10.1108/HFF-09-2023-0568

المؤلفون: M.E. Al-Atroush, A.E. Aboelela, Ezz El-Din Hemdan

المصدر: Journal of Rock Mechanics and Geotechnical Engineering, Vol 16, Iss 9, Pp 3812-3840 (2024)

مصطلحات موضوعية: Laterally loaded drilled shaft, load transfer and failure mechanisms, Physics-informed neural networks (PINNs), P-y curves, Artificial intelligence (AI), Dataset, Engineering geology. Rock mechanics. Soil mechanics. Underground construction, TA703-712

وصف الملف: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S167477552400180X; https://doaj.org/toc/1674-7755

URL الوصول: https://doaj.org/article/666d711e2e6743ff8705a8f3e9a91b9a

المؤلفون: Kazuya YOKOTA, Masataka OGURA, Masajiro ABE

المصدر: Mechanical Engineering Journal, Vol 11, Iss 5, Pp 24-00228-24-00228 (2024)

مصطلحات موضوعية: physics-informed neural networks (pinns), acoustic analysis, acoustic tube, inverse analysis, wave equation, Mechanical engineering and machinery, TJ1-1570

وصف الملف: electronic resource

Relation: https://www.jstage.jst.go.jp/article/mej/11/5/11_24-00228/_pdf/-char/en; https://doaj.org/toc/2187-9745

URL الوصول: https://doaj.org/article/1fc897ce9de84ae18f6876b03b8b9434

المؤلفون: Charles Z. Liu, Ying Zhang, Lu Qin, Yongfei Liu

المصدر: Applied Sciences, Vol 14, Iss 24, p 11618 (2024)

مصطلحات موضوعية: Finance-Informed Neural Networks (FINNs), Physics-Informed Neural Networks (PINNs), Kolmogorov–Arnold representation (KAR), option pricing, computational financial modeling, machine learning, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

وصف الملف: electronic resource

Relation: https://www.mdpi.com/2076-3417/14/24/11618; https://doaj.org/toc/2076-3417

URL الوصول: https://doaj.org/article/05698099a03b41a4aa159f49e3e786e4

المؤلفون: Rodrigo Hermosilla, Carlos Valle, Héctor Allende, Claudio Aguilar, Erich Lucic

المصدر: Applied Sciences, Vol 14, Iss 24, p 11686 (2024)

مصطلحات موضوعية: machine learning, semi-autogenous grinding (SAG) mills, convolutional neural networks (CNNs), physics-informed neural networks (PINNs), predictive maintenance, overload prediction, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

وصف الملف: electronic resource

Relation: https://www.mdpi.com/2076-3417/14/24/11686; https://doaj.org/toc/2076-3417

URL الوصول: https://doaj.org/article/14137aae7234491da68fde0a54684be7

المؤلفون: Fazl Ullah Fazal, Muhammad Sulaiman, David Bassir, Fahad Sameer Alshammari, Ghaylen Laouini

المصدر: Fractal and Fractional, Vol 8, Iss 11, p 671 (2024)

مصطلحات موضوعية: fractional Fokker–Planck–Levy equation, physics-informed neural networks (PINNs), finite difference method (FDM), Adams numerical technique, hybrid numerical method, fractional partial differential equations, Thermodynamics, QC310.15-319, Mathematics, QA1-939, Analysis, QA299.6-433

وصف الملف: electronic resource

Relation: https://www.mdpi.com/2504-3110/8/11/671; https://doaj.org/toc/2504-3110

URL الوصول: https://doaj.org/article/ebb0c41899ef4469b12e66ee9b2cdd96

المؤلفون: Rubén Darío Ortiz Ortiz, Oscar Martínez Núñez, Ana Magnolia Marín Ramírez

المصدر: Mathematics, Vol 12, Iss 21, p 3430 (2024)

مصطلحات موضوعية: boundary layer theory, Physics-Informed Neural Networks (PINNs), nonlinear partial differential equations, Burgers’ equation, shock waves, traveling waves, Mathematics, QA1-939

وصف الملف: electronic resource

Relation: https://www.mdpi.com/2227-7390/12/21/3430; https://doaj.org/toc/2227-7390

URL الوصول: https://doaj.org/article/3424975139454d0ba9edce01a587ef85

المؤلفون: Rigoni, Thomas, Arcieri, Giacomo, Haywood-Alexander, Marcus, id_orcid:0 000-0003-3384-2346, Haener, David, Chatzi, Eleni, id_orcid:0 000-0002-6870-240X

المصدر: ECCOMAS Congress 2024 - 9th European Congress on Computational Methods in Applied Sciences and Engineering

مصطلحات موضوعية: Ground Penetrating Radar (GPR), Physics-Informed Neural Networks (PINNs), Structural Health Monitoring (SHM), Railway Infrastructure

وصف الملف: application/application/pdf

Relation: http://hdl.handle.net/20.500.11850/714604

الاتاحة: https://hdl.handle.net/20.500.11850/714604
https://doi.org/10.3929/ethz-b-000714604

المؤلفون: Jianlin Huang, Rundi Qiu, Jingzhu Wang, Yiwei Wang

المصدر: Theoretical and Applied Mechanics Letters, Vol 14, Iss 2, Pp 100496- (2024)

مصطلحات موضوعية: Physics-informed neural networks (PINNs), Multi-scale, Fluid dynamics, Boundary layer, Engineering (General). Civil engineering (General), TA1-2040

وصف الملف: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2095034924000072; https://doaj.org/toc/2095-0349

URL الوصول: https://doaj.org/article/a7cc0f744e6a46bca8c7ae3c9b667674

المؤلفون: Ramirez, Ibai, Pino, Joel, Pardo, David, Sanz, Mikel, del Rio, Luis, Ortiz, Alvaro, Morozovska, Kateryna, 1992, Aizpurua, Jose I.

المصدر: Engineering applications of artificial intelligence. 139

مصطلحات موضوعية: Machine learning, Physics Informed Neural Networks (PINNs), Thermal modelling, Transformer

وصف الملف: print

URL الوصول: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-356307

المؤلفون: Ling, Hang Jung, Bru, Salomé, Puig, Julia, Vixège, Florian, Mendez, Simon, Nicoud, Franck, Courand, Pierre-Yves, Bernard, Olivier, Garcia, Damien

المساهمون: Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Imagerie Ultrasonore, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Institut Montpelliérain Alexander Grothendieck (IMAG), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Laboratoire des Écoulements Géophysiques et Industriels Grenoble (LEGI), Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (Fédération OSUG)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP), Université Grenoble Alpes (UGA), Institut universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), Hôpital de la Croix-Rousse CHU - HCL, Hospices Civils de Lyon (HCL), École doctorale MEGA (ED 162), ANR-11-LABX-0063,PRIMES,Physique, Radiobiologie, Imagerie Médicale et Simulation(2011), ANR-10-LABX-0060,CeLyA,Lyon Acoustics Centre(2010), ANR-11-IDEX-0007,Avenir L.S.E.,PROJET AVENIR LYON SAINT-ETIENNE(2011), ANR-21-CE19-0034,4D-iVFM,Vélocimétrie intraventriculaire 4-D par écho-Doppler(2021), ANR-22-CE45-0029,ORCHID,Diagnostic étiologique de maladies cardiaques basé sur les images échocardiographiques et les données cliniques(2022)

المصدر: ISSN: 0885-3010.

مصطلحات موضوعية: Cardiac flow, Color Doppler, Deep learning (DL), Echocardiography, Physics-guided neural networks (PGNNs), Physics-informed neural networks (PINNs), Ultrasound, Vector flow imaging, [SPI.MECA.MEFL]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Fluids mechanics [physics.class-ph], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system

Relation: info:eu-repo/semantics/altIdentifier/arxiv/2403.13040; info:eu-repo/semantics/altIdentifier/pmid/38857144; ARXIV: 2403.13040; PUBMED: 38857144

الاتاحة: https://hal.science/hal-04627945
https://hal.science/hal-04627945v1/document
https://hal.science/hal-04627945v1/file/Physics-Guided%20Neural%20Networks%20for%20Intraventricular%20Vector%20Flow%20Mapping.pdf
https://doi.org/10.1109/TUFFC.2024.3411718

المؤلفون: José Alberto Rodrigues

المصدر: Mathematics, Vol 12, Iss 8, p 1195 (2024)

مصطلحات موضوعية: physics-informed neural networks (PINNs), differential equation, loss function, activation function, deep learning, cancer cells, Mathematics, QA1-939

Relation: https://www.mdpi.com/2227-7390/12/8/1195; https://doaj.org/toc/2227-7390; https://doaj.org/article/89e9f157ebce4c638376591f9d5cfc34

الاتاحة: https://doi.org/10.3390/math12081195
https://doaj.org/article/89e9f157ebce4c638376591f9d5cfc34

المؤلفون: Andrii Pylypenko, Peter Demeter, Branislav Buľko, Slavomír Hubatka, Lukáš Fogaraš, Jaroslav Legemza, Jaroslav Demeter

المصدر: Engineering Proceedings, Vol 64, Iss 1, p 3 (2024)

مصطلحات موضوعية: pig iron, steelmaking, desulfurization, neural networks, Physics-Informed Neural Networks (PINNs), data analytics, Engineering machinery, tools, and implements, TA213-215

Relation: https://www.mdpi.com/2673-4591/64/1/3; https://doaj.org/toc/2673-4591; https://doaj.org/article/4ca449552a8b4e88bcff69080e121f22

الاتاحة: https://doi.org/10.3390/engproc2024064003
https://doaj.org/article/4ca449552a8b4e88bcff69080e121f22

المؤلفون: Zhang, Sheng, Lan, Peng, Li, Hai-Chao, Tong, Chen-Xi, Sheng, Daichao

المصدر: Engineering Computations, 2022, Vol. 39, Issue 7, pp. 2845-2865.

URL الوصول: http://www.emeraldinsight.com/doi/10.1108/EC-08-2021-0492

المؤلفون: Borrel-Jensen, Nikolas

المصدر: Borrel-Jensen , N 2023 , Accelerated methods for computing acoustic sound fields in dynamic virtual environments with moving sources . Technical University of Denmark .

مصطلحات موضوعية: Virtual acoustics, Physics-informed neural networks (PINNs), Neural operators, DeepONet, Transfer learning, Domain decomposition, Fourier methods, Spectral element method, High-performance computing, Real-time computing

وصف الملف: application/pdf

Relation: https://orbit.dtu.dk/en/publications/f4eae74c-2113-4784-98aa-358c833f0d26

الاتاحة: https://orbit.dtu.dk/en/publications/f4eae74c-2113-4784-98aa-358c833f0d26
https://backend.orbit.dtu.dk/ws/files/349938267/PhD_Thesis_166_sider_-_53_farvede_Nikolas_Borrel-Jensen.pdf

المؤلفون: Ali Imran Sandhu, Umair bin Waheed, Chao Song, Oliver Dorn, Pantelis Soupios

المصدر: Frontiers in Earth Science, Vol 11 (2023)

مصطلحات موضوعية: Helmholtz equation, physics informed neural networks (PINNs), wavefield modeling, seismic anisotropy, wave propagation, Science

وصف الملف: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/feart.2023.1227828/full; https://doaj.org/toc/2296-6463

URL الوصول: https://doaj.org/article/aef3f5fc3a3c45ef804f0dc299060ba4

المؤلفون: Pollok, Stefan, Olden-Jørgensen, Nataniel, Jørgensen, Peter Stanley, Bjørk, Rasmus

المصدر: Pollok , S , Olden-Jørgensen , N , Jørgensen , P S & Bjørk , R 2023 , ' Magnetic field prediction using generative adversarial networks ' , Journal of Magnetism and Magnetic Materials , vol. 571 , 170556 . https://doi.org/10.1016/j.jmmm.2023.170556

مصطلحات موضوعية: Deep learning (DL), Magnetic field prediction, Magnetostatics, Generative adversarial networks (GANs), Physics-informed neural networks (PINNs)

وصف الملف: application/pdf

Relation: https://orbit.dtu.dk/en/publications/aaa2d062-e60b-415a-937d-d20de3e5634d

الاتاحة: https://orbit.dtu.dk/en/publications/aaa2d062-e60b-415a-937d-d20de3e5634d
https://doi.org/10.1016/j.jmmm.2023.170556
https://backend.orbit.dtu.dk/ws/files/347757905/1-s2.0-S0304885323002056-main_1_.pdf

المؤلفون: Gratton, Serge, Mercier, Valentin, Riccietti, Elisa, Toint, Philippe, L

المساهمون: Algorithmes Parallèles et Optimisation (IRIT-APO), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Institut National Polytechnique (Toulouse) (Toulouse INP), Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS), Optimisation, Connaissances pHysiques, Algorithmes et Modèles (OCKHAM), Laboratoire de l'Informatique du Parallélisme (LIP), École normale supérieure de Lyon (ENS de Lyon), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon), Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut Rhône-Alpin des systèmes complexes (IXXI), Université de Lyon-Université de Lyon-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Inria Lyon, Institut National de Recherche en Informatique et en Automatique (Inria), Namur Center for Complex Systems Namur (NaXys), Université de Namur Namur (UNamur), ANR-19-P3IA-0004,ANITI,Artificial and Natural Intelligence Toulouse Institute(2019)

المصدر: https://hal.science/hal-04104450 ; 2023.

مصطلحات موضوعية: partial differential equations, nonlinear optimization, multi-level methods, physics-informed neural networks (PINNs), deep learning, [INFO]Computer Science [cs], [MATH]Mathematics [math]

الاتاحة: https://hal.science/hal-04104450
https://hal.science/hal-04104450v1/document
https://hal.science/hal-04104450v1/file/MPINNSv1.pdf

المؤلفون: Boulenc, Hugo, Bouclier, Robin, Garambois, Pierre-André, Monnier, Jerome

المساهمون: Institut de Mathématiques de Toulouse UMR5219 (IMT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Institut Clément Ader (ICA), Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-IMT École nationale supérieure des Mines d'Albi-Carmaux (IMT Mines Albi), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT), Risques, Ecosystèmes, Vulnérabilité, Environnement, Résilience (RECOVER), Aix Marseille Université (AMU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT), ANR-21-CE04-0021,MUFFINS,Prévision multiéchelle des inondations avec des solutions innovantes(2021)

المصدر: ECCOMAS 2024 - 9th European Congress on Computational Methods in Applied Sciences and Engineering ; https://hal.science/hal-04618150 ; ECCOMAS 2024 - 9th European Congress on Computational Methods in Applied Sciences and Engineering, Jun 2024, Lisbonne, Portugal ; https://eccomas2024.org/

مصطلحات موضوعية: Physics-Informed Neural Networks (PINNs), Inverse Problems, Parameter Identification, Shallow-Water Equations, Backwater Equation, [MATH]Mathematics [math]

جغرافية الموضوع: Lisbonne, Portugal

الاتاحة: https://hal.science/hal-04618150

المؤلفون: Zaharaddeen Karami Lawal, Hayati Yassin, Daphne Teck Ching Lai, Azam Che Idris

المصدر: Big Data and Cognitive Computing; Volume 6; Issue 4; Pages: 140

مصطلحات موضوعية: physics-informed neural networks (PINNs), partial differential equation (PDE), loss function, activation function, deep learning

وصف الملف: application/pdf

Relation: https://dx.doi.org/10.3390/bdcc6040140

الاتاحة: https://doi.org/10.3390/bdcc6040140