Results 31 to 40 of about 395,716 (259)
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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Reduced order method for finite difference modeling of cardiac propagation
Current Directions in Biomedical Engineering, 2020 Efficient numerical simulation of cardiac electrophysiology is crucial for studying the electrical properties of the heart tissue. The cardiac bidomain model is the most widely accepted representation of the electrical behaviour of the heart muscle.
Khan Riasat +2 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 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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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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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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Laguerre-Gram reduced-order modeling [PDF]
IEEE Transactions on Automatic Control, 2005 We present an efficient model reduction procedure based on the Laguerre description of the system to be approximated. Using a one-order operator defined in the Laplace domain we construct a pencil of functions and formulate the problem as the minimization of the L/sub /spl infin///sup 2/(/spl Ropf//sup +/) criterion. The use of a weight function in the
Ahmed Amghayrir +4 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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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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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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