Zobrazeno 1 - 10
of 167
pro vyhledávání: '"Ionel, M."'
Publikováno v:
Transylvanian Journal of Mathematics and Mechanics, Vol.15, No. 1-2, pp. 17-27, 2023
The present study focuses on a subject of significant interest in fluid dynamics: the identification of a model with decreased computational complexity from numerical code output using Koopman operator theory. A reduced-order modelling method that in
Externí odkaz:
http://arxiv.org/abs/2409.03549
Estimating probability of failure in aerospace systems is a critical requirement for flight certification and qualification. Failure probability estimation involves resolving tails of probability distribution, and Monte Carlo sampling methods are int
Externí odkaz:
http://arxiv.org/abs/2203.01436
Publikováno v:
In Journal of Computational Physics 15 November 2024 517
Autor:
Heaney, Claire E., Wolffs, Zef, Tómasson, Jón Atli, Kahouadji, Lyes, Salinas, Pablo, Nicolle, André, Matar, Omar K., Navon, Ionel M., Srinil, Narakorn, Pain, Christopher C.
The modelling of multiphase flow in a pipe presents a significant challenge for high-resolution computational fluid dynamics (CFD) models due to the high aspect ratio (length over diameter) of the domain. In subsea applications, the pipe length can b
Externí odkaz:
http://arxiv.org/abs/2202.06170
Publikováno v:
AIAA SciTech 2022 Forum
Dynamic mode decomposition (DMD) is an emerging methodology that has recently attracted computational scientists working on nonintrusive reduced order modeling. One of the major strengths that DMD possesses is having ground theoretical roots from the
Externí odkaz:
http://arxiv.org/abs/2201.04084
This work presents a hybrid modeling approach to data-driven learning and representation of unknown physical processes and closure parameterizations. These hybrid models are suitable for situations where the mechanistic description of dynamics of som
Externí odkaz:
http://arxiv.org/abs/2104.00114
Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows
Reduced rank nonlinear filters are increasingly utilized in data assimilation of geophysical flows, but often require a set of ensemble forward simulations to estimate forecast covariance. On the other hand, predictor-corrector type nudging approache
Externí odkaz:
http://arxiv.org/abs/2005.11296
We investigate the sensitivity of reduced order models (ROMs) to training data resolution as well as sampling rate. In particular, we consider proper orthogonal decomposition (POD), coupled with Galerkin projection (POD-GP), as an intrusive reduced o
Externí odkaz:
http://arxiv.org/abs/1910.07654
Generating a digital twin of any complex system requires modeling and computational approaches that are efficient, accurate, and modular. Traditional reduced order modeling techniques are targeted at only the first two but the novel non-intrusive app
Externí odkaz:
http://arxiv.org/abs/1910.07649
Publikováno v:
In Journal of Computational Science May 2023 69