Search Results - deep convolutional autoencoder finite element nonlinear (problem OR problemys)~

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

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

Source: Advances in Water Resources. 160:104098

Subject Terms: 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

Access 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

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

Index Terms: 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.

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

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

Subject Terms: 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

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

Index Terms: 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.

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

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

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

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

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

Source: 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

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

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

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

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

Subject Terms: 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.

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

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

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

Authors: Zhang, Wenyang Ayello, Francois Honegger, Doug et al.

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

Subject Terms: 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.

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

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

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

Manifold Learning

Authors: David Ryckelynck Fabien Casenave Nissrine Akkari

Resource Type: eBook.

Subjects: 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

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

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

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

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

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

Authors: Bao, Yanqing Mahadevan, Sankaran

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

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

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

Authors: Shusong H Monica S Bruno S

Source: 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.

Publication Type: Journal Article

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

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

Authors: Nikolopoulos, Stefanos Kalogeris, Ioannis Papadopoulos, Vissarion

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

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

Dimensionality reduction through convolutional autoencoders for fracture patterns prediction.

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

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

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

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

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

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

Subject Terms: *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.

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

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

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

Deep convolutional autoencoder thermography for artwork defect detection.

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

Source: 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.

Authors: Qu, Jiagang Zhao, Yijun Cai, Weihua

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

Subject Terms: PROPER orthogonal decomposition, REYNOLDS number, DECOMPOSITION method