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Randomized model order reduction [PDF]
Advances in Computational Mathematics, 2019 Singular value decomposition (SVD) has a crucial role in model order reduction. It is often utilized in the offline stage to compute basis functions that project the high-dimensional nonlinear problem into a low-dimensionsl model which is, then, evaluated cheaply. It constitutes a building block for many techniques such as e.g.
Alla A., Kutz J. N.
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Model Order Reduction of Microactuators: Theory and Application
Actuators, 2023 This paper provides an overview of techniques of compact modeling via model order reduction (MOR), emphasizing their application to cooperative microactuators.
Arwed Schütz, Tamara Bechtold
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A Review of Model Order Reduction Methods for Large-Scale Structure Systems
Shock and Vibration, 2021 The large-scale structure systems in engineering are complex, high dimensional, and variety of physical mechanism couplings; it will be difficult to analyze the dynamic behaviors of complex systems quickly and optimize system parameters.
Kuan Lu +7 more
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Model Order Reduction with Neural Networks: Application to Laminar and Turbulent Flows [PDF]
SN Computer Science, 2020 We investigate the capability of neural network-based model order reduction, i.e., autoencoder (AE), for fluid flows. As an example model, an AE which comprises of convolutional neural networks and multi-layer perceptrons is considered in this study. The
Kai Fukami +4 more
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Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows [PDF]
The Physics of Fluids, 2020 Unsteady fluid systems are nonlinear high-dimensional dynamical systems that may exhibit multiple complex phenomena in both time and space. Reduced Order Modeling (ROM) of fluid flows has been an active research topic in the recent decade with the ...
Hamidreza Eivazi +3 more
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State Residualisation and Kron Reduction for Model Order Reduction of Energy Systems
Applied Sciences, 2023 Greater numbers of power electronics (PEs) converters are being connected to energy systems due to the development of renewable energy sources, high-voltage transmission, and PE-interfaced loads. Given that power electronics-based devices and synchronous
Xianxian Zhao +4 more
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Parametric model-order-reduction development for unsteady convection
Frontiers in Physics, 2022 A time-averaged error indicator with POD-hGreedy is developed to drive parametric model order reduction (pMOR) for 2D unsteady natural convection in a high-aspect ratio slot parameterized with the Prandtl number, Rayleigh number, and slot angle with ...
Ping-Hsuan Tsai +2 more
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Rank-adaptive structure-preserving model order reduction of Hamiltonian systems [PDF]
ESAIM: Mathematical Modelling and Numerical Analysis, 2020 This work proposes an adaptive structure-preserving model order reduction method for finite-dimensional parametrized Hamiltonian systems modeling non-dissipative phenomena.
J. Hesthaven +2 more
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SOBMOR: Structured Optimization-Based Model Order Reduction [PDF]
SIAM Journal on Scientific Computing, 2020 Model order reduction (MOR) methods that are designed to preserve structural features of a given full order model (FOM) often suffer from a lower accuracy when compared to their non-structure-preserving counterparts. In this paper, we present a framework
Paul Schwerdtner, M. Voigt
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POD-Based Model-Order Reduction for Discontinuous Parameters
Fluids, 2022 Reduced-order models (ROMs) based on proper orthogonal decomposition (POD) are widely used in industry. Due to the rigid requirements on the input data, these methods struggle with discontinuous parameters, e.g., optional rear spoiler on a car.
Niklas Karcher
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