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S. Cheon, H. Kim, N. Nguyen, H. Cho, "A Study on Parametric Latent Dynamics Identification for Aerodynamic Flow Modelling", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Eighteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 10, Paper 9.1, 2025,

Keywords: potential flow, latent space dynamics identification, data-driven method, model order reduction, proper orthogonal decomposition, autoencoder.

Abstract

Learning physical models from full-order data has become an effective approach to reducing computational costs in various engineering problem. This paper presents a data-driven framework for dynamical modeling of parameterized vortex strength. The framework employed model-order reduction method with parametric sparse identification of nonlinear dynamics (SINDy) to construct a low-dimensional dynamical model. The training data covering the parameter space are represented by both linear and nonlinear model-order reduction method. The application of vortex panel method in vortex strength shows the effectiveness and accuracy of proposed framework.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 10 PROCEEDINGS OF THE EIGHTEENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING Edited by: P. Iványi, J. Kruis and B.H.V. Topping Paper 9.1 A Study on Parametric Latent Dynamics Identification for Aerodynamic Flow Modelling S. Cheon, H. Kim, N. Nguyen and H. Cho Jeonbuk National University, South of Korea Full Bibliographic Reference for this paper S. Cheon, H. Kim, N. Nguyen, H. Cho, "A Study on Parametric Latent Dynamics Identification for Aerodynamic Flow Modelling", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Eighteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 10, Paper 9.1, 2025, Keywords: potential flow, latent space dynamics identification, data-driven method, model order reduction, proper orthogonal decomposition, autoencoder. Abstract Learning physical models from full-order data has become an effective approach to reducing computational costs in various engineering problem. This paper presents a data-driven framework for dynamical modeling of parameterized vortex strength. The framework employed model-order reduction method with parametric sparse identification of nonlinear dynamics (SINDy) to construct a low-dimensional dynamical model. The training data covering the parameter space are represented by both linear and nonlinear model-order reduction method. The application of vortex panel method in vortex strength shows the effectiveness and accuracy of proposed framework. download the full-text of this paper (PDF, 8 pages, 574 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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