Results 41 to 50 of about 19,863,493 (281)
Regularization method for calibrated POD reduced-order models
MATEC Web of Conferences, 2014 In this work we present a regularization method to improve the accuracy of reduced-order models based on Proper Orthogonal Decomposition. The bench mark configuration retained corresponds to a case of relatively simple dynamics: a two-dimensional flow ...
El Majd Badr Abou, Cordier Laurent
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A parametric macromodelling technique [PDF]
, 2009 With the ever growing complexity of high-frequency systems in the electronic industry, formation of reduced-order models or compact macromodels of these systems is paramount.
Georgi G. Grahovski +3 more
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Generative adversarial reduced order modelling
Scientific ReportsAbstractIn this work, we present GAROM, a new approach for reduced order modeling (ROM) based on generative adversarial networks (GANs). GANs attempt to learn to generate data with the same statistics of the underlying distribution of a dataset, using two neural networks, namely discriminator and generator.
Coscia, Dario +2 more
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Determining Reduced Order Models for Optimal Stochastic Reduced Order Models
, 2015 The use of parameterized reduced order models(PROMs) within the stochastic reduced order model (SROM) framework is a logical progression for both methods. In this report, five different parameterized reduced order models are selected and critiqued against the other models along with truth model for the example of the Brake-Reuss beam. The models are: a
Matthew Bonney, Matthew Brake
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Reduced Order Modeling of an Industrial Feeder Model
IFAC Proceedings Volumes, 2003 Models of glaes furnaces are described by a set of nonlinear partial differential equâtioris which govern the mass, momentum and energy balances and a numberof non-linear functions of the independent scalais which describe the dependent variables like viscosities and densities in a fluid.
Astrid, P., Weiland, S., Twerda, A.
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New Regularization Method for Calibrated POD Reduced-Order Models
Mathematical Modelling and Analysis, 2016 Reduced-order models based on Proper orthogonal decomposition are known to suffer from a lack of accuracy due to the truncation effect introduced by keeping only the most energetic modes.
Badr Abou El Majd, Laurent Cordier
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Characteristics of linear modal instabilities in hypersonic flows with detached shock waves
Results in Engineering, 2021 A preliminary study on the linear instabilities present in a defined hypersonic flow over a blunt object is analyzed in this work. Such flow instabilities are defined in the detached shock wave and the defined shock region between the shock wave and the ...
José M. Pérez, Cristina Jimenez
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Reduced order models based on pod method for schrödinger equations
Mathematical Modelling and Analysis, 2013 Reduced-order models (ROM) are developed using the proper orthogonal decomposition (POD) for one dimensional linear and nonlinear Schrödinger equations. The main aim of this paper is to study the accuracy and robustness of the ROM approximations.
Gerda Jankevičiutė +3 more
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Linear Reduced-Order Model Predictive Control
IEEE Transactions on Automatic Control, 2022 Model predictive controllers use dynamics models to solve constrained optimal control problems. However, computational requirements for real-time control have limited their use to systems with low-dimensional models. Nevertheless, high-dimensional models arise in many settings, for example discretization methods for generating finite-dimensional ...
Joseph Lorenzetti +3 more
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Reduced Order Models Chapter - N.C. Clementi PhD Thesis (execution files)
, 2021 Execution files to reproduce the data for the Reduced Order Models Chapter - N.C. Clementi PhD Thesis. For instruction see README.md
Natalia C. Clementi
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