Diagnostic Goal-Driven Reduction of Multiscale Process Models
2006Fault detection and diagnosis in large-scale process systems is of great practical importance and present various challenging research problems at the same time. One of them is the computational complexity of the algorithms that causes an exponential growth of the computational resources (time and memory) with increasing system sizes.
Németh, E., Lakner, R., Hangos, K. M.
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A Stochastic Multiscale Model for Microstructure Model Reduction
2011Abstract : The mechanical properties of a deformed workpiece are sensitive to the initial microstructure. Often, the initial microstructure is random in nature and location specific. To model the variability of properties of the workpiece induced by variability in the initial microstructure, one needs to develop a reduced order stochastic input model ...
Nichols Zabaras, Bin Wen
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Multiscale Model Reduction with Generalized Multiscale Finite Element Methods in Geomathematics
2014In this chapter, we discuss multiscale model reduction using Generalized Multiscale Finite Element Methods (GMsFEM) in a number of geomathematical applications. GMsFEM has been recently introduced (Efendiev et al. 2012) and applied to various problems.
Efendiev, Yalchin R., Presho, Michael
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Dimensional Reduction of a Multiscale Continuum Model of Microtubule Gliding Assays
SIAM Journal on Applied Mathematics, 2014Microtubule gliding assays, in which molecular motors anchored to a plate drive the gliding motion of filaments in a quasi--two-dimensional fluid layer, have been shown to organize into a variety of large-scale patterns. We derive a fully three-dimensional multiscale coarse-grained model of a gliding assay including the evolution of densities of rigid ...
Christel Hohenegger +2 more
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Model Reduction of Multiscale Chemical Langevin Equations: A Numerical Case Study
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2009Two very important characteristics of biological reaction networks need to be considered carefully when modeling these systems. First, models must account for the inherent probabilistic nature of systems far from the thermodynamic limit. Often, biological systems cannot be modeled with traditional continuous-deterministic models.
Vassilios Sotiropoulos +3 more
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Multiscale Model Reduction Methods for Flow in Heterogeneous Porous Media
2016In this paper we provide a general framework for model reduction methods applied to fluid flow in porous media. Using reduced basis and numerical homogenization techniques we show that the complexity of the numerical approximation of Stokes flow in heterogeneous media can be drastically reduced.
Assyr Abdulle, Ondrej Budác
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A multi-stage deep learning based algorithm for multiscale model reduction
Journal of Computational and Applied Mathematics, 2021Eric T Chung +2 more
exaly
Multiscale modeling of linear elastic heterogeneous structures via localized model order reduction
International Journal for Numerical Methods in Engineering, 2023Philipp Diercks +2 more
exaly
REITERATED MULTISCALE MODEL REDUCTION USING THE GENERALIZED MULTISCALE FINITE ELEMENT METHOD
International Journal for Multiscale Computational Engineering, 2016Eric T. Chung +3 more
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MODEL REDUCTION APPROACHES IN MULTISCALE MODELING OF HETEROGENEOUS MATERIALS
International Journal for Multiscale Computational Engineering, 2013openaire +1 more source