Results 11 to 20 of about 395,716 (259)
Reduced Order Modeling Using Advection-Aware Autoencoders
Mathematical and Computational Applications, 2022 Physical systems governed by advection-dominated partial differential equations (PDEs) are found in applications ranging from engineering design to weather forecasting.
Sourav Dutta +3 more
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An Artificial Compression Reduced Order Model [PDF]
SIAM Journal on Numerical Analysis, 2020 We propose a novel artificial compression, reduced order model (AC-ROM) for the numerical simulation of viscous incompressible fluid flows. The new AC-ROM provides approximations not only for velocity, but also for pressure, which is needed to calculate forces on bodies in the flow and to connect the simulation parameters with pressure data. The new AC-
Victor P. DeCaria +4 more
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Component-Based Reduced Order Modeling of Large-Scale Complex Systems
Frontiers in Physics, 2022 Large-scale engineering systems, such as propulsive engines, ship structures, and wind farms, feature complex, multi-scale interactions between multiple physical phenomena.
Cheng Huang +2 more
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A Method to Minimize the Effort for Damper–Blade Matching Demonstrated on Two Blade Sizes
Applied Sciences, 2021 A method called PCR (Platform Centered Reduction) is designed to more effectively perform complex iterative and nonlinear calculations required for the dynamic response of turbine blades damped by dry friction contacts between rigid dampers and airfoil ...
Chiara Gastaldi, Muzio M. Gola
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A Bayesian Nonlinear Reduced Order Modeling Using Variational AutoEncoders
Fluids, 2022 This paper presents a new nonlinear projection based model reduction using convolutional Variational AutoEncoders (VAEs). This framework is applied on transient incompressible flows.
Nissrine Akkari +3 more
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Reduced-order modeling of hidden dynamics [PDF]
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016 5 pages, 2 ...
Héas, Patrick, Herzet, Cédric
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Reduced-order modelling numerical homogenization [PDF]
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2014 A general framework to combine numerical homogenization and reduced-order modelling techniques for partial differential equations (PDEs) with multiple scales is described. Numerical homogenization methods are usually efficient to approximate the effective solution of PDEs with multiple scales.
Abdulle Assyr, Bai Yun
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Lagrangian Reduced Order Modeling Using Finite Time Lyapunov Exponents
Fluids, 2020 There are two main strategies for improving the projection-based reduced order model (ROM) accuracy—(i) improving the ROM, that is, adding new terms to the standard ROM; and (ii) improving the ROM basis, that is, constructing ROM bases that yield more ...
Xuping Xie +4 more
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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
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