Results 41 to 50 of about 19,884,535 (281)
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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A Didactic Procedure to Solve the Equation of Steady-Static Response in Suspended Cables
Mathematics, 2020 Students in the electrical branch of the short-cycle tertiary education program acquire developmental and design skills for low voltage transmission power lines.
José Agüero-Rubio +3 more
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Multiphase flow applications of nonintrusive reduced-order models with Gaussian process emulation
Data-Centric Engineering, 2022 Reduced-order models (ROMs) are computationally inexpensive simplifications of high-fidelity complex ones. Such models can be found in computational fluid dynamics where they can be used to predict the characteristics of multiphase flows.
Themistoklis Botsas +3 more
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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 Model Approach to Inverse Scattering [PDF]
SIAM Journal on Imaging Sciences, 2020 We study an inverse scattering problem for a generic hyperbolic system of equations with an unknown coefficient called the reflectivity. The solution of the system models waves (sound, electromagnetic, or elastic), and the reflectivity models unknown scatterers embedded in a smooth and known medium.
Liliana Borcea +4 more
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