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Physics-driven proper orthogonal decomposition: A simulation methodology for partial differential equations. [PDF]
MethodsX, 2023 A simulation methodology derived from a learning algorithm based on Proper Orthogonal Decomposition (POD) is presented to solve partial differential equations (PDEs) for physical problems of interest.
Pulimeno A +6 more
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The Shifted Proper Orthogonal Decomposition: A Mode Decomposition for Multiple Transport Phenomena [PDF]
SIAM Journal of Scientific Computing, 2018 Transport-dominated phenomena provide a challenge for common mode-based model reduction approaches. We present a model reduction method, which is suited for these kind of systems. It extends the proper orthogonal decomposition (POD) by introducing time-dependent shifts of the snapshot matrix. The approach, called shifted proper orthogonal decomposition
Julius Reiss +2 more
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Gappy Spectral Proper Orthogonal Decomposition
SSRN Electronic Journal, 2022 Experimental spatio-temporal flow data often contain gaps or other types of undesired artifacts. To reconstruct flow data in the compromised or missing regions, a data completion method based on spectral proper orthogonal decomposition (SPOD) is developed.
Akhil Nekkanti, Oliver T. Schmidt
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Proper Orthogonal Decomposition of Flow Past Parallel Twin Cylinders
气体物理, 2023 The wake flow field around the parallel twin cylinders is complex, and a variety of complex wake patterns appear at different spacings. In this paper, based on the finite volume method, a numerical simulation of the flow around two-dimensional parallel ...
Jun-hui WANG, Shu-ling TIAN
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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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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 ...
Dan Yu, Suman Chakravorty
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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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Dissipation-optimized proper orthogonal decomposition
Physics of Fluids, 2023 We present a formalism for dissipation-optimized decomposition of the strain rate tensor (SRT) of turbulent flow data using Proper Orthogonal Decomposition (POD). The formalism includes a novel inverse spectral SRT operator allowing the mapping of the resulting SRT modes to corresponding velocity fields, which enables a complete dissipation-optimized ...
P. J. Olesen +4 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.
Christian Himpe +2 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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