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Reduced-order modeling of fluid flows with transformers
The Physics of Fluids, 2023Reduced-order modeling (ROM) of fluid flows has been an active area of research for several decades. The huge computational cost of direct numerical simulations has motivated researchers to develop more efficient alternative methods, such as ROMs and ...
AmirPouya Hemmasian +1 more
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Data-driven nonlinear reduced-order modeling of unsteady fluid–structure interactions
The Physics of Fluids, 2022A novel data-driven nonlinear reduced-order modeling framework is proposed for unsteady fluid–structure interactions (FSIs). In the proposed framework, a convolutional variational autoencoder model is developed to determine the coordinate transformation ...
Xinshuai Zhang +4 more
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Laguerre-SVD reduced order modeling
IEEE 8th Topical Meeting on Electrical Performance of Electronic Packaging (Cat. No.99TH8412), 2000A reduced-order modeling method based on a system description in terms of orthonormal Laguerre functions, together with a Krylov subspace decomposition technique is presented. The link with Pade approximation, the block Arnoldi process and singular value decomposition (SVD) leads to a simple and stable implementation of the algorithm. Novel features of
L. Knockaert, D. De Zutter
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Fluid Dynamics Research, 2020
We investigate the capability of machine learning (ML) based reduced order model (ML-ROM) for two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers.
K. Hasegawa +3 more
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We investigate the capability of machine learning (ML) based reduced order model (ML-ROM) for two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers.
K. Hasegawa +3 more
semanticscholar +1 more source
Reduced Order Stochastic Models
1988 American Control Conference, 1988This paper presents an approach to reduce the order of large-scale stochastic systems. The reduced-order model is obtained by considering only the stable modes through optimization of a steady-state error. Examples are given to illustrate the proposed method.
Craig S. Sims, Ali Feliachi
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Nonlinear unsteady bridge aerodynamics: Reduced-order modeling based on deep LSTM networks
, 2020Rapid increase in the bridge spans and the attendant innovative bridge deck cross-sections have placed significant importance on effectively modeling of the nonlinear, unsteady bridge aerodynamics.
Tao Li, Teng Wu, Zhao Liu
semanticscholar +1 more source
2005
Abstract In recent years, reduced-order modeling techniques have proven to be powerful tools for various problems in circuit simulation. For example, today, reduction techniques are routinely used to replace the large RCL subcircuits that model the interconnect or the pin package of VLSI circuits by models of much smaller dimension.
Zhaojun Bai +2 more
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Abstract In recent years, reduced-order modeling techniques have proven to be powerful tools for various problems in circuit simulation. For example, today, reduction techniques are routinely used to replace the large RCL subcircuits that model the interconnect or the pin package of VLSI circuits by models of much smaller dimension.
Zhaojun Bai +2 more
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Multi‐fidelity surrogate reduced‐order modeling of steady flow estimation
International Journal for Numerical Methods in Fluids, 2020A multi‐fidelity reduced‐order model (ROM), which incorporates low‐fidelity data to improve the prediction of high‐fidelity results, is proposed for the reconstruction of steady flow field at different conditions.
Xu Wang, J. Kou, Weiwei Zhang
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Journal of Computational Physics, 2019
A non-intrusive reduced-basis (RB) method is proposed for parametrized unsteady flows. A set of reduced basis functions are extracted from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD), and the coefficients of the ...
Qian Wang, J. Hesthaven, Deep Ray
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A non-intrusive reduced-basis (RB) method is proposed for parametrized unsteady flows. A set of reduced basis functions are extracted from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD), and the coefficients of the ...
Qian Wang, J. Hesthaven, Deep Ray
semanticscholar +1 more source
2014
In this chapter, the full-order state-space models presented in Chap. 3 are reduced in order and parametrized in the main parameters of the flight envelope. Order reduction is achieved by a multistep procedure: A modal reduction is followed by a reduction of the complete aeroelastic model and finally a balanced reduction is performed.
M. Valášek +3 more
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In this chapter, the full-order state-space models presented in Chap. 3 are reduced in order and parametrized in the main parameters of the flight envelope. Order reduction is achieved by a multistep procedure: A modal reduction is followed by a reduction of the complete aeroelastic model and finally a balanced reduction is performed.
M. Valášek +3 more
openaire +1 more source