Zobrazeno 1 - 10
of 171
pro vyhledávání: '"Navon, I. M."'
This paper presents a new data-driven non-intrusive reduced-order model(NIROM) that outperforms the traditional Proper orthogonal decomposition (POD) based reducedorder model. This is achieved by using Auto-Encoder(AE) and attention-based deep learni
Externí odkaz:
http://arxiv.org/abs/2109.02126
Considering the high computation cost produced in conventional computation fluid dynamic simulations, machine learning methods have been introduced to flow dynamic simulations in recent years. However, most of studies focus mainly on existing fluid f
Externí odkaz:
http://arxiv.org/abs/2003.10547
Deep learning techniques for improving fluid flow modelling have gained significant attention in recent years. Advanced deep learning techniques achieve great progress in rapidly predicting fluid flows without prior knowledge of the underlying physic
Externí odkaz:
http://arxiv.org/abs/2004.00707
We present an approach for forming sensitivity maps (or sensitivites) using ensembles. The method is an alternative to using an adjoint, which can be very challenging to formulate and also computationally expensive to solve. The main novelties of the
Externí odkaz:
http://arxiv.org/abs/1804.04457
The distance between the true and numerical solutions in some metric is considered as the discretization error magnitude. If error magnitude ranging is known, the triangle inequality enables the estimation of the vicinity of the approximate solution
Externí odkaz:
http://arxiv.org/abs/1708.04604
The issue of single-grid discretization error estimator, operating in the postprocessor mode, is addressed in the paper. An ensemble of numerical solutions, obtained using solvers of different accuracy, is shown to provide an upper estimate for the n
Externí odkaz:
http://arxiv.org/abs/1704.04994
Autor:
Bistrian, D. A., Navon, I. M.
This paper focuses on a new framework for reduced order modelling of non-intrusive data with application to 2D flows. To overcome the shortcomings of intrusive model order reduction usually derived by combining the POD and the Galerkin projection met
Externí odkaz:
http://arxiv.org/abs/1611.04884
Akademický článek
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Publikováno v:
SIAM Journal on Numerical Analysis, 2008 Jan 01. 47(1), 1-19.
Externí odkaz:
https://www.jstor.org/stable/25663111
Akademický článek
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