Results 21 to 30 of about 51,908 (255)
The use of proper orthogonal decomposition for the simulation of highly nonlinear hygrothermal performance [PDF]
MATEC Web of Conferences, 2019 In this paper, the use of proper orthogonal decomposition for simulating nonlinear heat, air and moisture transfer is investigated via two applications: HAMSTAD benchmarks 2 and 3.
Hou Tianfeng, Roels Staf, Janssen Hans
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Nonlinear proper orthogonal decomposition for convection-dominated flows [PDF]
The Physics of Fluids, 2021 Autoencoder techniques find increasingly common use in reduced order modeling as a means to create a latent space. This reduced order representation offers a modular data-driven modeling approach for nonlinear dynamical systems when integrated with a ...
Shady E. Ahmed +3 more
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Order reduction of matrix exponentials by proper orthogonal decomposition
Results in Applied Mathematics, 2023 Many applications of the matrix exponential exp(At) of a real matrix A and a real parameter t require repeated evaluation of it for different values of t.
Mohammad Dehghan Nayyeri +1 more
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Hierarchical Approximate Proper Orthogonal Decomposition [PDF]
SIAM Journal on Scientific Computing, 2018 Proper Orthogonal Decomposition (POD) is a widely used technique for the construction of low-dimensional approximation spaces from high-dimensional input data. For large-scale applications and an increasing amount of input data vectors, however, computing the POD often becomes prohibitively expensive.
Himpe, C., Leibner, T., Rave, S.
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Investigation of Proper Orthogonal Decomposition for Echo State Networks [PDF]
Neurocomputing, 2022 Echo State Networks (ESN) are a type of Recurrent Neural Network that yields promising results in representing time series and nonlinear dynamic systems. Although they are equipped with a very efficient training procedure, Reservoir Computing strategies,
J. Jordanou +3 more
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Non-Intrusive Reduced-Order Modeling Based on Parametrized Proper Orthogonal Decomposition
Energies, 2023 A new non-intrusive reduced-order modeling method based on space-time parameter decoupling for parametrized time-dependent problems is proposed. This method requires the preparation of a database comprising high-fidelity solutions.
Teng Li +4 more
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Spectral proper orthogonal decomposition [PDF]
Journal of Fluid Mechanics, 2016 The identification of coherent structures from experimental or numerical data is an essential task when conducting research in fluid dynamics. This typically involves the construction of an empirical mode base that appropriately captures the dominant flow structures.
Moritz Sieber +2 more
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Comparative Study on Modal Decomposition Methods of Unsteady Separated Flow in Compressor Cascade
Xibei Gongye Daxue Xuebao, 2020 The present work investigated the vortex structure and fluctuation frequency characteristics generated by boundary layer separation of a high-load compressor cascade using modal decomposition methods.
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Sparse sensor-based flow estimation with spectral proper orthogonal decomposition
AIP Advances, 2022 The application of Artificial Neural Networks (ANNs) in developing sensor-based estimators for unsteady flows has become an active area of research over the last decade.
Henrique Gambassi +2 more
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A randomized proper orthogonal decomposition technique [PDF]
2015 American Control Conference (ACC), 2015 In this paper, we consider the problem of model reduction of large scale systems, such as those obtained through the discretization of PDEs. We propose a randomized proper orthogonal decomposition (RPOD) technique to obtain the reduced order models by randomly choosing a subset of the inputs/outputs of the system to construct a suitable small sized ...
Yu, Dan, Chakravorty, Suman
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