Suchergebnisse - deep convolutional autoencoder finite element nonlinear problems*

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: Comparison with linear subspace techniques

Autoren: T. Kadeethum F. Ballarin Y. Choi et al.

Quelle: Advances in Water Resources. 160:104098

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Deep convolutional autoencoder, Reduced order modeling, Numerical Analysis (math.NA), Data-driven, 01 natural sciences, Machine Learning (cs.LG), Computational Engineering, Finance, and Science (cs.CE), Finite element, 0103 physical sciences, FOS: Mathematics, Mathematics - Numerical Analysis, 0101 mathematics, Computer Science - Computational Engineering, Finance, and Science, Non-intrusive, Nonlinear problem

Zugangs-URL: http://arxiv.org/abs/2107.11460
https://hdl.handle.net/10807/202829
https://doi.org/10.1016/j.advwatres.2021.104098

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: Comparison with linear subspace techniques

Autoren: Kadeethum, T. Ballarin, Francesco Choi, Y. et al.

Index Begriffe: Data-driven, Deep convolutional autoencoder, Finite element, Non-intrusive, Nonlinear problem, Reduced order modeling, Settore MAT/08 - ANALISI NUMERICA, info:eu-repo/semantics/article

URL: https://hdl.handle.net/10807/202829
info:eu-repo/semantics/altIdentifier/wos/WOS:000803685400003
volume:160
issue:NA
firstpage:104098
lastpage:N/A
issueyear:2022
journal:ADVANCES IN WATER RESOURCES

Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning.

Autoren: Kadeethum, Teeratorn Ballarin, Francesco O'Malley, Daniel et al.

Quelle: Scientific Reports; 11/30/2022, Vol. 12 Issue 1, p1-18, 18p

Schlagwörter: SUPERVISED learning, DEEP learning, PROPER orthogonal decomposition, NATURAL heat convection, BENCHMARK problems (Computer science), POROUS materials

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques

Autoren: Kadeethum, T. Ballarin, F. Choi, Y. et al.

Index Begriffe: Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning, Mathematics - Numerical Analysis, text

URL: http://arxiv.org/abs/2107.11460

Stochastic electrical resistivity tomography with ensemble smoother and deep convolutional autoencoders.

Autoren: Aleardi, Mattia Vinciguerra, Alessandro Stucchi, Eusebio et al.

Quelle: Near Surface Geophysics; Apr2022, Vol. 20 Issue 2, p160-177, 18p

Schlagwörter: ELECTRICAL resistance tomography, GEOPHYSICS, DEEP learning, MATHEMATICAL optimization, ITERATIVE methods (Mathematics)

Model‐Reduced Adjoint‐Based Inversion Using Deep‐Learning: Example of Geological Carbon Sequestration Modeling.

Autoren: Xiao, Cong Zhang, Shicheng Ma, Xinfang et al.

Quelle: Water Resources Research; Jan2022, Vol. 58 Issue 1, p1-27, 27p

Schlagwörter: GEOLOGICAL carbon sequestration, MULTIPHASE flow, FINITE difference method, REDUCED-order models, GEOLOGICAL modeling, DEEP learning, MAGNITUDE (Mathematics), WORKFLOW

Prediction of cross-sectional features of SPR joints based on the punch force-displacement curve using machine learning.

Autoren: Ferrándiz, Borja Daoud, Monzer Kohout, Nicolas et al.

Quelle: International Journal of Advanced Manufacturing Technology; Oct2023, Vol. 128 Issue 9/10, p4023-4034, 12p, 1 Color Photograph, 1 Black and White Photograph, 6 Charts, 10 Graphs

Schlagwörter: MACHINE learning, SPOT welding, MECHANICAL behavior of materials, RIVETED joints, FINITE element method

Embedding physical knowledge in deep neural networks for predicting the phonon dispersion curves of cellular metamaterials.

Autoren: Wang, Zihan Xian, Weikang Li, Ying et al.

Quelle: Computational Mechanics; Jul2023, Vol. 72 Issue 1, p221-239, 19p

Schlagwörter: ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, PHONONS, METAMATERIALS, DISPERSION (Chemistry)

Deep learning methods for blood flow reconstruction in a vessel with contrast enhanced x-ray computed tomography.

Autoren: Shusong H Monica S Bruno S

Quelle: International journal for numerical methods in biomedical engineering [Int J Numer Method Biomed Eng] 2024 Jan; Vol. 40 (1), pp. e3785. Date of Electronic Publication: 2023 Oct 25.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Wiley Country of Publication: England NLM ID: 101530293 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2040-7947 (Electronic) Linking ISSN: 20407939 NLM ISO Abbreviation: Int J Numer Method Biomed Eng Subsets: MEDLINE

MeSH-Schlagworte: Deep Learning*, Tomography, X-Ray Computed/methods ; Hemodynamics ; X-Rays ; Image Processing, Computer-Assisted/methods ; Algorithms

Wheel impact test by deep learning: prediction of location and magnitude of maximum stress.

Autoren: Shin, Seungyeon Jin, Ah-hyeon Yoo, Soyoung et al.

Quelle: Structural & Multidisciplinary Optimization; Jan2023, Vol. 66 Issue 1, p1-20, 20p

Deep ensemble inverse model for image-based estimation of solar cell parameters.

Autoren: Battaglia, M. Comi, E. Stadelmann, T. et al.

Quelle: APL Machine Learning; Mar2023, Vol. 1 Issue 1, p1-13, 13p

Schlagwörter: SOLAR cells, ELECTROLUMINESCENCE, CONVOLUTIONAL neural networks, DEEP learning, FABRICATION (Manufacturing)

Non-intrusive surrogate modeling for parametrized time-dependent partial differential equations using convolutional autoencoders.

Autoren: Nikolopoulos, Stefanos Kalogeris, Ioannis Papadopoulos, Vissarion

Quelle: Engineering Applications of Artificial Intelligence. Mar2022, Vol. 109, pN.PAG-N.PAG. 1p.

Schlagwörter: *PARTIAL differential equations, *MONTE Carlo method, *STOCHASTIC analysis, *FINITE element method, *DEEP learning, *DECODING algorithms

Dimensionality reduction through convolutional autoencoders for fracture patterns prediction.

Autoren: Shinde, Krushna Itier, Vincent Mennesson, José et al.

Quelle: Applied Mathematical Modelling. Feb2023, Vol. 114, p94-113. 20p.

Schlagwörter: *DEEP learning, *CRACK propagation, *STRUCTURAL mechanics, *FRACTURE mechanics, *BRITTLE fractures, *PRINCIPAL components analysis, *REDUCED-order models

Manifold Learning

Autoren: David Ryckelynck Fabien Casenave Nissrine Akkari

Resource Type: eBook.

Schlagworte: Continuum mechanics, Deep learning (Machine learning), Structural analysis (Engineering)

Categories: MATHEMATICS / Probability & Statistics / General, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Mathematical & Statistical Software, SCIENCE / Physics / Mathematical & Computational, SCIENCE / Mechanics / Thermodynamics, TECHNOLOGY & ENGINEERING / Industrial Engineering

Assessment of unsteady flow predictions using hybrid deep learning based reduced-order models.

Autoren: Bukka, Sandeep Reddy Gupta, Rachit Magee, Allan Ross et al.

Quelle: Physics of Fluids. Jan2021, Vol. 33 Issue 1, p1-43. 43p.

Schlagwörter: *REDUCED-order models, *BLENDED learning, *DEEP learning, *RECURRENT neural networks, *PROPER orthogonal decomposition, *UNSTEADY flow

Deep Convolutional Neural Network-Based Structural Damage Localization and Quantification Using Transmissibility Data.

Autoren: Cofre-Martel, Sergio Kobrich, Philip Lopez Droguett, Enrique et al.

Quelle: Shock & Vibration; 9/8/2019, p1-27, 27p

Schlagwörter: SUPERVISED learning, IMAGE processing, STRUCTURAL health monitoring, ASSET management, FEATURE extraction, WOODEN beams

Deep convolutional autoencoder thermography for artwork defect detection.

Autoren: Liu, Yi Wang, Fumin Liu, Kaixin et al.

Quelle: Quantitative InfraRed Thermography Journal; Nov2024, Vol. 21 Issue 6, p367-383, 17p

Nonlinear dynamic mode decomposition from time-resolving snapshots based on deep convolutional autoencoder.

Autoren: Qu, Jiagang Zhao, Yijun Cai, Weihua

Quelle: Physics of Fluids; Jun2023, Vol. 35 Issue 6, p1-15, 15p

Schlagwörter: PROPER orthogonal decomposition, REYNOLDS number, DECOMPOSITION method

Optimal approximations for the free boundary problems of the space-time fractional Black-Scholes equations using a combined physics-informed neural network.

Autoren: Song L Tan Y Yu F et al.

Quelle: Scientific reports [Sci Rep] 2024 Oct 25; Vol. 14 (1), pp. 25289. Date of Electronic Publication: 2024 Oct 25.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE

Road crack detection interpreting background images by convolutional neural networks and a self‐organizing map.

Autoren: Yamaguchi, Takahiro Mizutani, Tsukasa

Quelle: Computer-Aided Civil & Infrastructure Engineering; Jun2024, Vol. 39 Issue 11, p1616-1640, 25p

Schlagwörter: CONVOLUTIONAL neural networks, SELF-organizing maps, FEATURE extraction, DEEP learning, PAVEMENTS