Results 41 to 50 of about 7,720,158 (325)
Data-driven reduced order modeling for mechanical oscillators using Koopman approaches
Frontiers in Applied Mathematics and Statistics, 2023 Data-driven reduced order modeling methods that aim at extracting physically meaningful governing equations directly from measurement data are facing a growing interest in recent years. The HAVOK-algorithm is a Koopman-based method that distills a forced,
Charlotte Geier +4 more
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Reduced order modeling of fluid flows using convolutional neural networks
Journal of Fluid Science and Technology, 2023 Application of machine learning is currently one of the hottest topics in the fluid mechanics field. While machine learning seems to have a great possibility, its limitations should also be clarified.
Koji FUKAGATA
doaj +1 more source
Reduced order modeling of delayed PEEC circuits [PDF]
, 2011 We propose a novel model order reduction technique that is able to accurately reduce electrically large systems with delay elements, which can be described by means of neutral delayed differential equations.
Antonini, Giulio +5 more
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A deep learning enabler for nonintrusive reduced order modeling of fluid flows [PDF]
The Physics of Fluids, 2019 In this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows.
Suraj Pawar +5 more
semanticscholar +1 more source
Reduced-order modeling for unsteady transonic flows around an airfoil [PDF]
, 2008 High-transonic unsteady flows around an airfoil at zero angle of incidence and moderate Reynolds numbers are characterized by an unsteadiness induced by the von Kármán instability and buffet phenomenon interaction.
Bourguet, Rémi +2 more
core +3 more sources
Computational fluid dynamics modeling of a wafer etch temperature control system
Digital Chemical Engineering, 2023 Next-generation etching processes for semiconductor manufacturing exploit the potential of a variety of operating conditions, including cryogenic conditions at which high etch rates of silicon and very low etch rates of the photoresist are achieved. Thus,
Henrique Oyama +5 more
doaj +1 more source
A Comparison of Data‐Driven Approaches to Build Low‐Dimensional Ocean Models
Journal of Advances in Modeling Earth Systems, 2021 We present a comprehensive inter‐comparison of linear regression (LR), stochastic, and deep‐learning approaches for reduced‐order statistical emulation of ocean circulation.
Niraj Agarwal +4 more
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Neural Network-Based Model Reduction of Hydrodynamics Forces on an Airfoil
Fluids, 2021 In this paper, an artificial neural network (ANN)-based reduced order model (ROM) is developed for the hydrodynamics forces on an airfoil immersed in the flow field at different angles of attack.
Hamayun Farooq +3 more
doaj +1 more source
CD-ROM: Complemented Deep - Reduced order model
Computer Methods in Applied Mechanics and Engineering, 2023 Model order reduction through the POD-Galerkin method can lead to dramatic gains in terms of computational efficiency in solving physical problems. However, the applicability of the method to non linear high-dimensional dynamical systems such as the Navier-Stokes equations has been shown to be limited, producing inaccurate and sometimes unstable models.
Menier, Emmanuel +4 more
openaire +6 more sources
Pressure Stabilization Strategies for a LES Filtering Reduced Order Model
Fluids, 2021 We present a stabilized POD–Galerkin reduced order method (ROM) for a Leray model. For the implementation of the model, we combine a two-step algorithm called Evolve-Filter (EF) with a computationally efficient finite volume method.
Michele Girfoglio +2 more
doaj +1 more source