Results 31 to 40 of about 14,700,185 (245)
Convolutional Autoencoders for Reduced-Order Modeling
CoRR, 2021 In the construction of reduced-order models for dynamical systems, linear projection methods, such as proper orthogonal decompositions, are commonly employed. However, for many dynamical systems, the lower dimensional representation of the state space can most accurately be described by a \textit{nonlinear} manifold.
Sreeram Venkat +2 more
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Vortex-induced vibration of catenary riser: reduced-order modeling and lock-in analysis using wake oscillator [PDF]
, 2009 A new reduced-order model capable of analyzing the vortex-induced vibration of catenary riser in the ocean current has been developed. This semi analytical-numerical approach is versatile and allows for a significant reduction in computational effort for
O'Brien, P. +4 more
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Wall‐based reduced‐order modelling [PDF]
International Journal for Numerical Methods in Fluids, 2015 SummaryIn this work, we propose a novel approach to model order reduction for incompressible fluid flows, which focuses on the spatio‐temporal description of the stresses on the surface of a body, that is, of the wall shear stress and of the wall pressure.
Lasagna, Davide, Tutty, Owen
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Reduced Order Modeling Methods for Turret - Gun System [PDF]
, 1990 Reduced-order modeling techniques are used to design lower order robust controllers for a turret-gun system. The balance-truncation, Routh approximation, and L. Litz\u27s (1980) modal techniques are used to derive the models. A critical comparison of the
Rao, Vittal S. +3 more
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Reduced-Order Model Approaches for Predicting Airfoil Performance
ActuatorsThis study delves into the construction of reduced-order models (ROMs) of a flow field over a NACA 0012 airfoil at a moderate Reynolds number and an angle of attack of 8∘. Numerical simulations were computed through the finite-volume solver OpenFOAM. The
Antonio Colanera +3 more
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Reduced Order Modeling with Skew-Radial Basis Functions for Time Series Prediction
Engineering Proceedings, 2023 We present a sparsity-promoting RBF algorithm for time-series prediction. We use a time-delayed embedding framework and model the function from the embedding space to predict the next point in the time series.
Manuchehr Aminian, Michael Kirby
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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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Advanced Modeling and Simulation in Engineering Sciences, 2022 Most of the methods used today for handling local stress constraints in topology optimization, fail to directly address the non-self-adjointness of the stress-constrained topology optimization problem.
Manyu Xiao +4 more
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