Zobrazeno 1 - 10
of 19 575
pro vyhledávání: '"Variational data assimilation"'
Autor:
Wang, Mengze, Zaki, Tamer A.
Estimation of near-wall turbulence in channel flow from outer observations is investigated using adjoint-variational data assimilation. We first consider fully resolved velocity data, starting at a distance from the wall. By enforcing the estimated f
Externí odkaz:
http://arxiv.org/abs/2410.09916
The four-dimensional variational data assimilation (4D-Var) has emerged as an important methodology, widely used in numerical weather prediction, oceanographic modeling, and climate forecasting. Classical unconstrained gradient-based algorithms often
Externí odkaz:
http://arxiv.org/abs/2410.04471
A hybrid 4D-variational data assimilation method for chaotic climate models is introduced using the Lorenz '63 model. This approach aims to optimise an Earth system model (ESM), for which no adjoint exists, by utilising an adjoint model of a differen
Externí odkaz:
http://arxiv.org/abs/2403.03166
Autor:
Benaceur, Amina a, Verfürth, Barbara b, ⁎
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 December 2024 432 Part B
This paper presents a novel centralized, variational data assimilation approach for calibrating transient dynamic models in electrical power systems, focusing on load model parameters. With the increasing importance of inverter-based resources, asses
Externí odkaz:
http://arxiv.org/abs/2311.07676
The Reynolds-averaged Navier-Stokes (RANS) equations provide a computationally efficient method for solving fluid flow problems in engineering applications. However, the use of closure models to represent turbulence effects can reduce their accuracy.
Externí odkaz:
http://arxiv.org/abs/2310.11543
In variational assimilation, the most probable state of a dynamical system under Gaussian assumptions for the prior and likelihood can be found by solving a least-squares minimization problem . In recent years, we have seen the popularity of hybrid v
Externí odkaz:
http://arxiv.org/abs/2306.11869
Autor:
Huynh, Ngo Nghi Truyen, Garambois, Pierre-André, Colleoni, François, Renard, Benjamin, Roux, Hélène
Publikováno v:
Colloque SHF 2023 - Pr{\'e}vision des crues et des inondations, Nov 2023, Toulouse, France
Tackling the difficult problem of estimating spatially distributed hydrological parameters, especially for floods on ungauged watercourses, this contribution presents a novel seamless regionalization technique for learning complex regional transfer f
Externí odkaz:
http://arxiv.org/abs/2307.02497
Autor:
Benaceur, Amina, Verfürth, Barbara
This paper is a contribution in the context of variational data assimilation combined with statistical learning. The framework of data assimilation traditionally uses data collected at sensor locations in order to bring corrections to a numerical mod
Externí odkaz:
http://arxiv.org/abs/2305.04734
Multilevel estimators aim at reducing the variance of Monte Carlo statistical estimators, by combining samples generated with simulators of different costs and accuracies. In particular, the recent work of Schaden and Ullmann (2020) on the multilevel
Externí odkaz:
http://arxiv.org/abs/2306.07017