Results 11 to 20 of about 1,287,681 (259)
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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Learning stable reduced-order models for hybrid twins
Data-Centric Engineering, 2021 The concept of “hybrid twin” (HT) has recently received a growing interest thanks to the availability of powerful machine learning techniques. This twin concept combines physics-based models within a model order reduction framework—to obtain real-time ...
Abel Sancarlos +6 more
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Topology Optimization Based Material Design for 3D Domains Using MATLAB
Applied Sciences, 2022 In this work, a simple, easy to use MATLAB code is presented for the optimal design of materials for 3D domains. For the optimal design of materials, the theoretical framework of topology optimization and that of homogenization were utilized to develop a
George Kazakis, Nikos D. Lagaros
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Reduced Order Models for the Quasi-Geostrophic Equations: A Brief Survey
Fluids, 2020 Reduced order models (ROMs) are computational models whose dimension is significantly lower than those obtained through classical numerical discretizations (e.g., finite element, finite difference, finite volume, or spectral methods).
Changhong Mou +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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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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Approximate deconvolution reduced order modeling [PDF]
Computer Methods in Applied Mechanics and Engineering, 2017 This paper proposes a large eddy simulation reduced order model(LES-ROM) framework for the numerical simulation of realistic flows. In this LES-ROM framework, the proper orthogonal decomposition(POD) is used to define the ROM basis and a POD differential filter is used to define the large ROM structures.
Xie, X. +3 more
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Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models
Space Weather, 2023 The geospace environment is volatile and highly driven. Space weather has effects on Earth's magnetosphere that cause a dynamic and enigmatic response in the thermosphere, particularly on the evolution of neutral mass density.
Richard J. Licata, Piyush M. Mehta
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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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Deep Learning-Based Accuracy Upgrade of Reduced Order Models in Topology Optimization
Applied Sciences, 2021 Topology optimization problems pose substantial requirements in computing resources, which become prohibitive in cases of large-scale design domains discretized with fine finite element meshes.
Nikos Ath. Kallioras +2 more
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