Suchergebnisse - 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 von Kadeethum, T., Ballarin, F., Choi, Y., O’Malley, D., Yoon, H., Bouklas, N.

“… Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g …”
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Dimensionality reduction through convolutional autoencoders for fracture patterns prediction von Shinde, Krushna, Itier, Vincent, Mennesson, José, Vasiukov, Dmytro, Shakoor, Modesar

“… •Dimensionality reduction using a convolutional neural network autoencoder.•Application to highly nonlinear brittle fracture mechanics problems …”
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Non-intrusive surrogate modeling for parametrized time-dependent partial differential equations using convolutional autoencoders von Nikolopoulos, Stefanos, Kalogeris, Ioannis, Papadopoulos, Vissarion

“… from the problem’s parametric space to its solution space. For this purpose, training data are collected by solving the high-fidelity model via finite elements for a reduced set of parameter values …”
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Reduced-order modeling for stochastic large-scale and time-dependent flow problems using deep spatial and temporal convolutional autoencoders von Abdedou, Azzedine, Soulaimani, Azzeddine

“… A non-intrusive reduced-order model based on convolutional autoencoders is proposed as a data-driven tool to build an efficient nonlinear reduced-order model for stochastic spatiotemporal large-scale flow problems …”
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An Efficient Acoustic Metamaterial Design Approach Integrating Attention Mechanisms and Autoencoder Networks von Chu, Yangyang, Liu, Yiping, Wang, Bingke, Zhang, Zhifeng

“… However, there exists a highly nonlinear mapping relationship between their structural parameters and performance responses, which causes traditional design methods to face the problems …”
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Reduced-order modeling for stochastic large-scale and time-dependent problems using deep spatial and temporal convolutional autoencoders von Abdedou, Azzedine, Soulaïmani, Azzeddine

“… A non-intrusive reduced order model based on convolutional autoencoders (NIROM-CAEs) is proposed as a data-driven tool to build an efficient nonlinear reduced-order model for stochastic spatio-temporal large-scale physical problems …”
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Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques von Kadeethum, T, Ballarin, F, Choi, Y, O'Malley, D, Yoon, H, Bouklas, N

“… ). Here, we present a non-intrusive reduced order model of natural convection in porous media employing deep convolutional autoencoders for the compression and reconstruction and either radial basis function (RBF …”
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Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning von Kadeethum, Teeratorn, Ballarin, Francesco, O’Malley, Daniel, Choi, Youngsoo, Bouklas, Nikolaos, Yoon, Hongkyu

“… ) using deep-convolutional autoencoders (DC–AE) has been shown to capture nonlinear solution manifolds but fails to perform adequately when linear subspace approaches such as proper orthogonal decomposition (POD) would be optimal …”
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Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning von Kadeethum, Teeratorn, Ballarin, Francesco, O'Malley, Daniel, Choi, Youngsoo, Bouklas, Nikolaos, Yoon, Hongkyu

“… ) using deep-convolutional autoencoders (DC-AE) has been shown to capture nonlinear solution manifolds but fails to perform adequately when linear subspace approaches such as proper orthogonal decomposition (POD) would be optimal …”
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A novel end-to-end deep learning model for predicting the full stress field of tensioned membrane structures von Xu, Junhao, Sheng, LingYu, Zhang, Yingying, Fei, Shuhuan, Zhao, Ziang

“… A deep learning model consisting of two essential modules was presented. The stress encoding-decoding module employs convolutional autoencoders (CAE …”
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Assessment of unsteady flow predictions using hybrid deep learning based reduced order models von Sandeep Reddy Bukka, Gupta, Rachit, Allan Ross Magee, Jaiman, Rajeev Kumar

“… The first model projects the high-fidelity time series data from a finite element Navier-Stokes solver to a low-dimensional subspace via proper orthogonal decomposition (POD …”
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