Výsledky vyhľadávania - (dynamical OR dynamik) system autoencoder*

A novel process monitoring approach based on variational recurrent autoencoder Autor Cheng, Feifan, He, Q. Peter, Zhao, Jinsong

“…Modern manufacturing plants demand not only more intelligent but also safer and more reliable process monitoring systems…”
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A novel process monitoring approach based on Feature Points Distance Dynamic Autoencoder Autor Cheng, Feifan, Zhao, Jinsong

“… And autoencoder can not be ensured to get various meaningful features from the raw data. In this work, we proposed Feature Points Distance Dynamic Autoencoder (FPDDAE…”
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Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders Autor Lee, Kookjin, Carlberg, Kevin T.

“…•Two model-reduction methods that project dynamical systems on nonlinear manifolds…”
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Learning nonlinear projections for reduced-order modeling of dynamical systems using constrained autoencoders Autor Otto, Samuel E, Macchio, Gregory R, Rowley, Clarence W

“…Recently developed reduced-order modeling techniques aim to approximate nonlinear dynamical systems on low-dimensional manifolds learned from data…”
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Aligning Dynamic Social Networks: An Optimization Over Dynamic Graph Autoencoder Autor Sun, Li, Zhang, Zhongbao, Wang, Feiyang, Ji, Pengxin, Wen, Jian, Su, Sen, Yu, Philip S.

“… Towards this end, we propose a novel Dynamic Graph autoencoder based dynamic social network Alignment approach, referred to as DGA , unfolding the fruitful dynamics of social networks for user alignment…”
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Temporally-consistent koopman autoencoders for forecasting dynamical systems Autor Nayak, Indranil, Chakrabarti, Ananda, Kumar, Mrinal, Teixeira, Fernando L., Goswami, Debdipta

“…Absence of sufficiently high-quality data often poses a key challenge in data-driven modeling of high-dimensional spatio-temporal dynamical systems…”
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Unsupervised Speech Enhancement Using Dynamical Variational Autoencoders Autor Bie, Xiaoyu, Leglaive, Simon, Alameda-Pineda, Xavier, Girin, Laurent

“…Dynamical variational autoencoders (DVAEs) are a class of deep generative models with latent variables, dedicated to model time series of high-dimensional data…”
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A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder Autor Kim, Youngkyu, Choi, Youngsoo, Widemann, David, Zohdi, Tarek

“…•A novel physics-informed neural network reduced order model is introduced.•It is based on nonlinear manifold solution representation.•A sparse shallow neural…”
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Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics Autor Xu, Jiayang, Duraisamy, Karthik

“…A data-driven framework is proposed towards the end of predictive modeling of complex spatio-temporal dynamics, leveraging nested non-linear manifolds…”
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Learning the health index of complex systems using dynamic conditional variational autoencoders Autor Wei, Yupeng, Wu, Dazhong, Terpenny, Janis

“…Recent advances in sensing technologies have enabled engineers to collect big data to predict the remaining useful life (RUL) of complex systems…”
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Dynamical polynomial-based self-organizing neural networks designed through autoencoder-driven feature selection and adaptive neuron pruning Autor Wang, Zhen, Oh, Sung-Kwun, Fu, Zunwei, Roh, Seok-Beom, Kim, Eun-Hu, Kim, Jin-Yul

“…In this study, we introduce a dynamical polynomial-based self-organizing neural network (DPSON…”
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GD-VAEs: Geometric dynamic variational autoencoders for learning nonlinear dynamics and dimension reductions Autor Lopez, Ryan, Atzberger, Paul J.

“…•Development of deep variational autoencoders utilizing prior knowledge from qualitative analysis of dynamical systems…”
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Abnormality Monitoring in the Blast Furnace Ironmaking Process Based on Stacked Dynamic Target-Driven Denoising Autoencoders Autor Jiang, Ke, Jiang, Zhaohui, Xie, Yongfang, Pan, Dong, Gui, Weihua

“… Thus, this article proposes a novel stacked dynamic target-driven denoising autoencoder for layer-by-layer hierarchical feature representation, and the dynamic relationship between samples…”
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Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions Autor Conti, Paolo, Gobat, Giorgio, Fresca, Stefania, Manzoni, Andrea, Frangi, Attilio

“… Starting from a limited amount of full order solutions, the proposed approach leverages autoencoder neural networks with parametric sparse identification of nonlinear dynamics…”
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Dynamical System Autoencoders Autor He, Shiquan, Paffenroth, Randy, Cava, Olivia, Dunham, Cate

“… In this paper, we introduce a new type of autoencoder that we call dynamical system autoencoder (DSAE…”
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Stability Analysis of Denoising Autoencoders Based on Dynamical Projection System Autor Park, Saerom, Lee, Jaewook

“…In this study, we give a stability analysis of denoising autoencoder(DAE) from the novel perspective of dynamical systems when the input density is defined as a distribution on a manifold…”
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Predicting turbulent dynamics with the convolutional autoencoder echo state network Autor Racca, Alberto, Doan, Nguyen Anh Khoa, Magri, Luca

“…The dynamics of turbulent flows is chaotic and difficult to predict. This makes the design of accurate reduced-order models challenging…”
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Weighted dynamic network link prediction based on graph autoencoder Autor Mei, Peng, Zhao, Yuhong, Wang, Jingyu, Liang, Yefei

“…With the development of deep learning, Graph Autoencoders (GAE) within unsupervised learning frameworks have been widely applied to representation learning in dynamic networks…”
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Φ-DVAE: Physics-informed dynamical variational autoencoders for unstructured data assimilation Autor Glyn-Davies, Alex, Duffin, Connor, Deniz Akyildiz, O., Girolami, Mark

“… To address these shortcomings, in this paper we develop a physics-informed dynamical variational autoencoder (Φ-DVAE…”
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Stochastic embeddings of dynamical phenomena through variational autoencoders Autor García, Constantino A., Félix, Paulo, Presedo, Jesús M., Otero, Abraham

“…System identification in scenarios where the observed number of variables is less than the degrees of freedom in the dynamics is an important challenge…”
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