Search Results - 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

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)

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

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

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

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)

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)

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

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

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

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

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

Authors: Yamaguchi, Takahiro Mizutani, Tsukasa

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

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

Bridging Deep Convolutional Autoencoders and Ensemble Smoothers for Improved Estimation of Channelized Reservoirs.

Authors: Sebacher, Bogdan Toma, Stefan Adrian

Source: Mathematical Geosciences; Jul2022, Vol. 54 Issue 5, p903-939, 37p