Zobrazeno 1 - 10
of 218
pro vyhledávání: '"Cinnella, Paola"'
A machine learning-based methodology for blending data-driven turbulent closures for the Reynolds-Averaged Navier-Stokes (RANS) equations is proposed to improve the generalizability across different flow scenarios. Data-driven models based on sparse
Externí odkaz:
http://arxiv.org/abs/2410.14431
Autor:
Yagoubi, Mouadh, Danan, David, Leyli-abadi, Milad, Brunet, Jean-Patrick, Mazari, Jocelyn Ahmed, Bonnet, Florent, gmati, maroua, Farjallah, Asma, Cinnella, Paola, Gallinari, Patrick, Schoenauer, Marc
The integration of machine learning (ML) techniques for addressing intricate physics problems is increasingly recognized as a promising avenue for expediting simulations. However, assessing ML-derived physical models poses a significant challenge for
Externí odkaz:
http://arxiv.org/abs/2407.01641
Autor:
Vinuesa, Ricardo, Rabault, Jean, Azizpour, Hossein, Bauer, Stefan, Brunton, Bingni W., Elofsson, Arne, Jarlebring, Elias, Kjellstrom, Hedvig, Markidis, Stefano, Marlevi, David, Cinnella, Paola, Brunton, Steven L.
Technological advancements have substantially increased computational power and data availability, enabling the application of powerful machine-learning (ML) techniques across various fields. However, our ability to leverage ML methods for scientific
Externí odkaz:
http://arxiv.org/abs/2405.04161
Autor:
Cinnella, Paola
This chapter provides an introduction to data-driven techniques for the development and calibration of closure models for the Reynolds-Averaged Navier--Stokes (RANS) equations. RANS models are the workhorse for engineering applications of computation
Externí odkaz:
http://arxiv.org/abs/2404.09074
In this manuscript, we combine non-intrusive reduced order models (ROMs) with space-dependent aggregation techniques to build a mixed-ROM. The prediction of the mixed formulation is given by a convex linear combination of the predictions of some prev
Externí odkaz:
http://arxiv.org/abs/2403.05710
A stochastic Machine-Learning approach is developed for data-driven Reynolds-Averaged Navier-Stokes (RANS) predictions of turbulent flows, with quantified model uncertainty. This is done by combining a Bayesian symbolic identification methodology for
Externí odkaz:
http://arxiv.org/abs/2306.16996
Publikováno v:
International Journal of Numerical Methods for Heat & Fluid Flow, 2023, Vol. 34, Issue 7, pp. 2808-2831.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/HFF-09-2023-0551
In this article, we propose a data-driven methodology for combining the solutions of a set of competing turbulence models. The individual model predictions are linearly combined for providing an ensemble solution accompanied by estimates of predictiv
Externí odkaz:
http://arxiv.org/abs/2301.09013
Publikováno v:
36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks
Surrogate models are necessary to optimize meaningful quantities in physical dynamics as their recursive numerical resolutions are often prohibitively expensive. It is mainly the case for fluid dynamics and the resolution of Navier-Stokes equations.
Externí odkaz:
http://arxiv.org/abs/2212.07564
The dynamics of a shock wave impinging on a transitional high-enthalpy boundary layer out of thermochemical equilibrium is investigated for the first time by means of a direct numerical simulation. The freestream Mach number is equal to 9 and the obl
Externí odkaz:
http://arxiv.org/abs/2212.06939