Long-term prediction of nonlinear hydrodynamics in bubble columns by using artificial neural networks

Lin, Haiyan; Chen, W.; Tsutsumi, Atsushi

doi:10.1016/s0255-2701(02)00210-6

Cited by 20 publications

(9 citation statements)

References 18 publications

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“…However, chaotic systems are susceptible to initial and boundary conditions. Therefore, an accurate long-term prediction requires careful selection and pre-processing of the features [146]; otherwise, the error for highly chaotic systems increases with continuously longer times [147,148]. More recently, regression forests [149], as well LSTM [150] have been used for transient flow physics.…”

Section: Surrogate Modellingmentioning

confidence: 99%

Machine-Learning Methods for Computational Science and Engineering

2020

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The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing. In this paper, we provide a review of the state-of-the-art in ML for computational science and engineering. We discuss ways of using ML to speed up or improve the quality of simulation techniques such as computational fluid dynamics, molecular dynamics, and structural analysis. We explore the ability of ML to produce computationally efficient surrogate models of physical applications that circumvent the need for the more expensive simulation techniques entirely. We also discuss how ML can be used to process large amounts of data, using as examples many different scientific fields, such as engineering, medicine, astronomy and computing. Finally, we review how ML has been used to create more realistic and responsive virtual reality applications.

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Section: Surrogate Modellingmentioning

confidence: 99%

Machine-Learning Methods for Computational Science and Engineering

2020

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“…For a chaotic system, since it is extremely difficult to achieve accurate long time predictions, models are most often evaluated in a variety of ways. These include subjective visual inspection [21] or measures for the attractor [2] such as maximum Lyapunov exponent [17], correlation dimension and other time averaged characteristics [28]. The first approach, although perhaps the most widely used [30][39] [49], can sometimes be misleading [15].…”

Section: Model Selectionmentioning

confidence: 99%

Data-Driven Discovery of Closure Models

Pan¹,

Duraisamy²

2018

SIAM J. Appl. Dyn. Syst.

107

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Derivation of reduced order representations of dynamical systems requires the modeling of the truncated dynamics on the retained dynamics. In its most general form, this so-called closure model has to account for memory effects. In this work, we present a framework of operator inference to extract the governing dynamics of closure from data in a compact, non-Markovian form. We employ sparse polynomial regression and artificial neural networks to extract the underlying operator. For a special class of non-linear systems, observability of the closure in terms of the resolved dynamics is analyzed and theoretical results are presented on the compactness of the memory. The proposed framework is evaluated on examples consisting of linear to nonlinear systems with and without chaotic dynamics, with an emphasis on predictive performance on unseen data.

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“…In short, ANN was suitable for diagnosing and predicting without the limitation of linear and nonlinear relationships. So it has been widely used in solving different problems, including optimisation (Becerikli, Konar, & Samad, 2003;Chang, Chang, & Huang, 2005), distinguishing and classification (Marchant & Onyango, 2003;Han & Xi, 2004), prediction (Ambrozic & Turk, 2003;Lin, Chen, & Tsutsumi, 2003;Sahoo, Ray, & Wade, 2005), approximation (Liang & Palakal, 2002;Ghiassi, Saidan, & Zimbra, 2005;Yang & Chang, 2005), etc.…”

Section: Artificial Neural Network and Back-propagation Neural Networkmentioning

confidence: 99%

Looking for potential service quality gaps to improve customer satisfaction by using a new GA approach

Tsai¹,

Chen²,

Chan³

et al. 2011

Total Quality Management & Business Excellence

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Gap analysis (GA) is one of the most popular techniques which has been widely used in service marketing to help businesses make customer satisfaction (CS) decisions through a service quality perspective. However, an assumption of a linear relationship between attribute performance and CS underlies the application of the GA approach. And this assumption is challenged by Kano's two-dimensional model which indicates the possible phenomenon of a non-linear relationship between attribute performance and CS. Therefore, the main purpose of this study is to propose a novel GA approach through Kano's two-dimensional conception with the application of the neural network technique in order to extract the actual contribution offered by each attribute for CS improvement. A comparative analysis with the traditional GA approach and our proposed approach was performed by using an empirical study on a Taiwanese Human Resource service online agency. Results showed that the new proposed GA approach has higher effectiveness in reflecting the impact of gap reduced on CS improvement. This implies that the new GA approach can be considered as a helpful decision-making technique for CS decision making. More implications from empirical research were discussed.

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Long-term prediction of nonlinear hydrodynamics in bubble columns by using artificial neural networks

Cited by 20 publications

References 18 publications

Machine-Learning Methods for Computational Science and Engineering

Machine-Learning Methods for Computational Science and Engineering

Data-Driven Discovery of Closure Models

Looking for potential service quality gaps to improve customer satisfaction by using a new GA approach

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