Search Results - Reduced Convolutional Autoencoder

Non-linear Manifold Reduced-Order Models with Convolutional Autoencoders and Reduced Over-Collocation Method by Romor, Francesco, Stabile, Giovanni, Rozza, Gianluigi

“… reduced-order models based on linear subspace approximations. Among the possible solutions, there are purely data-driven methods that leverage autoencoders…”
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Non‐intrusive reduced‐order modeling using convolutional autoencoders by Halder, Rakesh, Fidkowski, Krzysztof J., Maki, Kevin J.

“… In this work, we present a non‐intrusive ROM framework for steady‐state parameterized partial differential equations that uses convolutional autoencoders to provide a nonlinear…”
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Coupling of a lightweight model of reduced convolutional autoencoder with linear SVM classifier to detect brain tumours on FPGA by Chatterjee, Soumita, Pandit, Soumya, Das, Arpita

“… Following this preprocessing step, a dual-stack Reduced Convolutional Autoencoder (RCA) unit is coupled with a linear Support Vector Machine (SVM) classifier…”
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Epileptic Seizure Recognition Using Reduced Deep Convolutional Stack Autoencoder and Improved Kernel RVFLN From EEG Signals by Sahani, Mrutyunjaya, Rout, Susanta Kumar, Dash, Pradipta Kishor

“…In this paper, reduced deep convolutional stack autoencoder (RDCSAE) and improved kernel random vector functional link network (IKRVFLN…”
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Reduced-order modeling via convolutional autoencoder for emulating combustion of hydrogen/methane fuel blends by Ding, Siyu, Ni, Chenxu, Chu, Xu, Lu, Qingzhou, Wang, Xingjian

“… This study presents parametric reduced order models (ROMs) that leverage deep neural network-based dimension reduction through a convolutional autoencoder (AE…”
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Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical system by Teutsch, Philipp, Pfeffer, Philipp, Sharifi Ghazijahani, Mohammad, Cierpka, Christian, Schumacher, Jörg, Mäder, Patrick

“… Within the context of reduced-order models, convolutional autoencoders (CAEs) pose a universally applicable alternative to conventional approaches…”
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Precise single step and multistep short-term photovoltaic parameters forecasting based on reduced deep convolutional stack autoencoder and minimum variance multikernel random vector functional network by Sahani, Mrutyunjaya, Choudhury, Sasmita, Siddique, Marif Daula, Parida, Tanmoy, Dash, Pradipta Kishore, Panda, Sanjib Kumar

“… To address this, we have developed a novel hybrid model: a reduced deep convolutional stack autoencoder with a minimum variance multikernel random vector functional link network (RDCSAE-MVMRVFLN…”
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Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: Comparison with linear subspace techniques by Kadeethum, T., Ballarin, F., Choi, Y., O’Malley, D., Yoon, H., Bouklas, N.

“…, the process of CO2 sequestration). Here, we extend and present a non-intrusive reduced order model of natural convection in porous media employing deep convolutional…”
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PREDICTION OF AIRFOIL TABLE UNDER SUBSONIC FLOW CONDITIONS VIA CONVOLUTIONAL AUTOENCODER-BASED REDUCED-ORDER MODELING by Kang, Y.E., Lee, D., Yee, K.

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Novel attention-based convolutional autoencoder and ConvLSTM for reduced-order modeling in fluid mechanics with time derivative architecture by Beiki, Alireza, Kamali, Reza

“…To construct reduced-order models, we propose a convolutional autoencoder and a convolutional LSTM (CAE-ConvLSTM…”
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Reduced Biquaternion Stacked Denoising Convolutional AutoEncoder for RGB-D Image Classification by Huang, Xiang, Gai, Shan

“… To address these problems, this letter proposes a novel RGB-D image classification framework based on reduced biquaternion stacked denoising convolutional autoencoder (RQ-SDCAE…”
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A turbulence reduced order model based on non-interpolated convolutional autoencoder by WU, Pin, ZHANG, Bo, SONG, Chao, ZHOU, Zhu

“… Convolutional autoencoders necessitate uniform interpolation across the flow field to attain a uniform flow field snapshot, yet this process frequently introduces interpolation errors and unwarranted temporal overheads…”
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A graph convolutional autoencoder approach to model order reduction for parametrized PDEs by Pichi, Federico, Moya, Beatriz, Hesthaven, Jan S.

“…The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM…”
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A turbulence reduced order model based on non-interpolated convolutional autoencoder by WU, Pin, ZHANG, Bo, SONG, Chao, Zhu, ZHOU

“… Convolutional autoencoders necessitate uniform interpolation across the flow field to attain a uniform flow field snapshot, yet this process frequently introduces interpolation errors and unwarranted…”
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Convolutional Autoencoders for Reduced-Order Modeling by Sreeram Venkat, Smith, Ralph C, Kelley, Carl T

“… and are often problem-specific \citep[see][]{carlberg_ca}. Here, we utilize randomized training data to create and train convolutional autoencoders that perform nonlinear dimension reduction for the wave and Kuramoto-Shivasinsky equations…”
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Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation by Mack, Julian, Arcucci, Rossella, Molina-Solana, Miguel, Guo, Yi-Ke

“…We propose a new ‘Bi-Reduced Space’ approach to solving 3D Variational Data Assimilation using Convolutional Autoencoders…”
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Reduced-order modeling for stochastic large-scale and time-dependent flow problems using deep spatial and temporal convolutional autoencoders by 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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APPLICATION OF REDUCED ORDER MODEL FOR METHANE JET FLAME BASED ON DEEP CONVOLUTIONAL AUTOENCODER by Lee, W.J., Huh, K.Y.

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Data-driven non-intrusive reduced order modelling of selective laser melting additive manufacturing process using proper orthogonal decomposition and convolutional autoencoder by Chaudhry, Shubham, Abdedou, Azzedine, Soulaïmani, Azzeddine

“…) and a convolutional autoencoder-multilayer perceptron (CAE-MLP). The POD-ANN model utilizes proper orthogonal decomposition to create a reduced-order model, which is then…”
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Non-linear manifold ROM with Convolutional Autoencoders and Reduced Over-Collocation method by Romor, Francesco, Stabile, Giovanni, Rozza, Gianluigi

“… reduced-order models based on linear subspace approximations. Among the possible solutions, there are purely data-driven methods that leverage autoencoders…”
Get full text