Results 11 to 20 of about 3,862 (118)
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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Multifidelity computing for coupling full and reduced order models.
PLoS ONE, 2021 Hybrid physics-machine learning models are increasingly being used in simulations of transport processes. Many complex multiphysics systems relevant to scientific and engineering applications include multiple spatiotemporal scales and comprise a ...
Shady E Ahmed +4 more
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Genetic algorithm-based calibration of reduced order galerkin models
Mathematical Modelling and Analysis, 2011 Low-dimensional models, allowing quick prediction of fluid behaviour, are key enablers of closed-loop flow control. Reduction of the model's dimension and inconsistency of high-fidelity data set and the reduced-order formulation lead to the decrease of ...
Witold Stankiewicz +2 more
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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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Uncertainty Analysis of Neutron Diffusion Eigenvalue Problem Based on Reduced-order Model
Yuanzineng kexue jishu, 2023 In order to improve the efficiency of core physical uncertainty analysis based on sampling statistics, the proper orthogonal decomposition (POD) and Galerkin projection method were combined to study the application feasibility of reduced-order model ...
In order to improve the efficiency of core physical uncertainty analysis based on sampling statistics, the proper orthogonal decomposition (POD) and Galerkin projection method were combined to study the application feasibility of reduced-order model based on POD-Galerkin method in core physical uncertainty analysis. The two-dimensional two group TWIGL benchmark question was taken as the research object, the key variation characteristics of the core flux distribution were extracted under the finite perturbation of the group constants of each material region, and the full-order neutron diffusion problem was projected on the variation characteristics to establish a reduced-order neutron diffusion model. The reduced-order model was used to replace the full-order model to carry out the uncertainty analysis of the group constants of the material region. The results show that the bias of the mathematical expectation of keff calculated by reduced-order and full-order models is close to 1 pcm. In addition, compared with the calculation time required for uncertainty analysis of full-order model, the analysis time of reduced-order model (including the calculation time of the full-order model required for the construction of reduced-order model) is only 11.48%, which greatly improves the efficiency of uncertainty analysis. The biases of mathematical expectation of keff calculated by reduced-order and full-order models based on Latin hypercube sampling and simple random sampling are less than 8 pcm, and under the same sample size, the bias from the Latin hypercube sampling result is smaller. From the TWIGL benchmark test results, under the same sample size, Latin hypercube sampling method is more recommended for POD-Galerkin reduced-order model.
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Modern methods of mathematical modeling of blood flow using reduced order methods [PDF]
Компьютерные исследования и моделирование, 2018 The study of the physiological and pathophysiological processes in the cardiovascular system is one of the important contemporary issues, which is addressed in many works.
Sergey Sergeevich Simakov
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Simultaneous Regression and Selection in Nonlinear Modal Model Identification
Vibration, 2021 High fidelity finite element (FE) models are widely used to simulate the dynamic responses of geometrically nonlinear structures. The high computational cost of running long time duration analyses, however, has made nonlinear reduced order models (ROMs ...
Christopher Van Damme +3 more
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