Výsledky vyhľadávania - Deep convolutional autoencoder Finite element Nonlinear problem

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

Autori: T. Kadeethum F. Ballarin Y. Choi a ďalší

Zdroj: Advances in Water Resources. 160:104098

Predmety: 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

Prístupová URL adresa: 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

Autori: Kadeethum, T. Ballarin, Francesco Choi, Y. a ďalší

Témy: 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.

Autori: Kadeethum, Teeratorn Ballarin, Francesco O'Malley, Daniel a ďalší

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

Predmety: 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

Autori: Kadeethum, T. Ballarin, F. Choi, Y. a ďalší

Témy: 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.

Autori: Aleardi, Mattia Vinciguerra, Alessandro Stucchi, Eusebio a ďalší

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

Predmety: 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.

Autori: Xiao, Cong Zhang, Shicheng Ma, Xinfang a ďalší

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

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

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

Autori: Shin, Seungyeon Jin, Ah-hyeon Yoo, Soyoung a ďalší

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

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

Autori: Ferrándiz, Borja Daoud, Monzer Kohout, Nicolas a ďalší

Zdroj: 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

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

Machine learning‐based prediction of the seismic response of fault‐crossing natural gas pipelines.

Autori: Zhang, Wenyang Ayello, Francois Honegger, Doug a ďalší

Zdroj: Earthquake Engineering & Structural Dynamics; Sep2023, Vol. 52 Issue 11, p3238-3255, 18p

Predmety: NATURAL gas pipelines, MACHINE learning, SEISMIC response, INFRASTRUCTURE (Economics), FINITE element method, STATISTICAL models, REGRESSION analysis

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

Autori: Wang, Zihan Xian, Weikang Li, Ying a ďalší

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

Predmety: 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.

Autori: Shusong H Monica S Bruno S

Zdroj: 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.

Spôsob vydávania: Journal Article

Informácie o časopise: 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

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

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

Autori: Battaglia, M. Comi, E. Stadelmann, T. a ďalší

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

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

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

Autori: Nikolopoulos, Stefanos Kalogeris, Ioannis Papadopoulos, Vissarion

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

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

Diagnosis of interior damage with a convolutional neural network using simulation and measurement data.

Autori: Bao, Yanqing Mahadevan, Sankaran

Zdroj: Structural Health Monitoring; Sep2022, Vol. 21 Issue 5, p2312-2328, 17p

Predmety: CONVOLUTIONAL neural networks, ARTIFICIAL neural networks, DEEP learning, STRUCTURAL health monitoring, INHOMOGENEOUS materials, SURFACE cracks

Dimensionality reduction through convolutional autoencoders for fracture patterns prediction.

Autori: Shinde, Krushna Itier, Vincent Mennesson, José a ďalší

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

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

Manifold Learning

Autori: David Ryckelynck Fabien Casenave Nissrine Akkari

Typ zdroja: eBook.

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

Kategórie: 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.

Autori: Bukka, Sandeep Reddy Gupta, Rachit Magee, Allan Ross a ďalší

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

Predmety: *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.

Autori: Cofre-Martel, Sergio Kobrich, Philip Lopez Droguett, Enrique a ďalší

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

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

Deep convolutional autoencoder thermography for artwork defect detection.

Autori: Liu, Yi Wang, Fumin Liu, Kaixin a ďalší

Zdroj: 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.

Autori: Qu, Jiagang Zhao, Yijun Cai, Weihua

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

Predmety: PROPER orthogonal decomposition, REYNOLDS number, DECOMPOSITION method