Výsledky vyhledávání - "Adil Siripatana"

Bayesian inference of spatially varying Manning’s n coefficients in an idealized coastal ocean model using a generalized Karhunen-Loève expansion and polynomial chaos

Autor: Omar M. Knio, Olivier Le Maitre, Clint Dawson, Adil Siripatana, Ibrahim Hoteit

Publikováno v: Ocean Dynamics
Ocean Dynamics, Springer Verlag, 2020, 70 (8), pp.1103-1127. ⟨10.1007/s10236-020-01382-4⟩
Ocean Dynamics, 2020, 70 (8), pp.1103-1127. ⟨10.1007/s10236-020-01382-4⟩

Bayesian inference with coordinate transformations and polynomial chaos for a Gaussian process with a parametrized prior covariance model was introduced in Sraj et al. (Comput Methods Appl Mech Eng 298:205–228, 2016a) to enable and infer uncertaint

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::095d823541f37bf60a81727e2096561f
https://hal.archives-ouvertes.fr/hal-03010151/file/main.pdf

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Assessing an ensemble Kalman filter inference of Manning’s n coefficient of an idealized tidal inlet against a polynomial chaos-based MCMC

Autor: Talea Mayo, Olivier Le Maitre, Ibrahim Hoteit, Ihab Sraj, Clint Dawson, Omar M. Knio, Adil Siripatana

Publikováno v: Ocean Dynamics. 67:1067-1094

Bayesian estimation/inversion is commonly used to quantify and reduce modeling uncertainties in coastal ocean model, especially in the framework of parameter estimation. Based on Bayes rule, the posterior probability distribution function (pdf) of th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0f0b33f3b7774297d1375e3c23444b5b
https://doi.org/10.1007/s10236-017-1074-z

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Combining Ensemble Kalman Filter and Multiresolution Analysis for Efficient Assimilation into Adaptive Mesh Models

Autor: Adil Siripatana, Loïc Giraldi, Ibrahim Hoteit, Omar M. Knio, O. P. Le Maître

Publikováno v: Computational Geosciences
Computational Geosciences, Springer Verlag, 2019, 23 (6), pp.1259-1276. ⟨10.1007/s10596-019-09882-z⟩

International audience; A new approach is developed for data assimilation into adaptive mesh models with the ensemble Kalman filter (EnKF). The EnKF is combined with a wavelet-based multiresolution analysis (MRA) scheme to enable robust and efficient

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::67f2bf48933a6c91ea340656f7094593
https://hal.archives-ouvertes.fr/hal-02330745/file/main.pdf

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Ensemble Kalman filter inference of spatially-varying Manning’s n coefficients in the coastal ocean

Autor: Clint Dawson, Omar M. Knio, Talea Mayo, Olivier Le Maitre, Ibrahim Hoteit, Adil Siripatana

Publikováno v: Journal of Hydrology
Journal of Hydrology, Elsevier, 2018, 562, pp.664-684. ⟨10.1016/j.jhydrol.2018.05.021⟩

This work was supported by the King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia grant number CRG3-2016.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6389a8298f43b9152372682327d87e5
https://hal.archives-ouvertes.fr/hal-02337083

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Single-site Lennard-Jones models via polynomial chaos surrogates of Monte Carlo molecular simulation

Autor: Adil Siripatana, Shuyu Sun, Ibrahim Hoteit, Omar M. Knio, Ahmad Salim Kadoura

Publikováno v: The Journal of chemical physics. 144(21)

In this work, two Polynomial Chaos (PC) surrogates were generated to reproduce Monte Carlo (MC) molecular simulation results of the canonical (single-phase) and the NVT-Gibbs (two-phase) ensembles for a system of normalized structureless Lennard-Jone

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cba8b4f4331f5756e3c582457d651388
https://pubmed.ncbi.nlm.nih.gov/27276951

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The development of interactive 3D spring visualization for periodic multidimensional direction time-series data sets

Autor: Krisanadej Jaroensutasinee, Supakit Prueksaaroon, Adil Siripatana, Sornthep Vannarat

Publikováno v: 2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

this paper introduces a new approach for visualizing multidimentional weather-direction-related time-series data sets called “3D Spring Model”. Spring Model is designed to visualize pattern behind large time-series weather data set and to clearif

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d409034fbeb6a5068a8e96c59e64b96a
https://doi.org/10.1109/ecticon.2012.6254257

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