Zobrazeno 1 - 10
of 456
pro vyhledávání: '"A. Taverniers"'
Autor:
Taverniers, Søren, Korneev, Svyatoslav, Somarakis, Christoforos, Behandish, Morad, Lew, Adrian J.
The computation of damping rates of an oscillating fluid with a free surface in which viscosity is small and surface tension high is numerically challenging. A typical application requiring such computation is drop-on-demand (DoD) microfluidic device
Externí odkaz:
http://arxiv.org/abs/2307.00094
A major challenge in developing accurate and robust numerical solutions to multi-physics problems is to correctly model evolving discontinuities in field quantities, which manifest themselves as interfaces between different phases in multi-phase flow
Externí odkaz:
http://arxiv.org/abs/2302.03898
Publikováno v:
AAAI-ADAM-2022: Association for the Advancement of Artificial Intelligence (AAAI) 2022 Workshop on AI for Design and Manufacturing (ADAM)
Predicting part quality for additive manufacturing (AM) processes requires high-fidelity numerical simulation of partial differential equations (PDEs) governing process multiphysics on a scale of minimum manufacturable features. This makes part-scale
Externí odkaz:
http://arxiv.org/abs/2202.03665
Timely completion of design cycles for complex systems ranging from consumer electronics to hypersonic vehicles relies on rapid simulation-based prototyping. The latter typically involves high-dimensional spaces of possibly correlated control variabl
Externí odkaz:
http://arxiv.org/abs/2009.04570
We introduce the concept of a Graph-Informed Neural Network (GINN), a hybrid approach combining deep learning with probabilistic graphical models (PGMs) that acts as a surrogate for physics-based representations of multiscale and multiphysics systems
Externí odkaz:
http://arxiv.org/abs/2006.14807
We design and implement a novel algorithm for computing a multilevel Monte Carlo (MLMC) estimator of the cumulative distribution function of a quantity of interest in problems with random input parameters or initial conditions. Our approach combines
Externí odkaz:
http://arxiv.org/abs/1906.00126
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
In Journal of Computational Physics 1 November 2021 444
Publikováno v:
In Journal of Computational Physics 15 May 2021 433
Publikováno v:
In Journal of Computational Physics 15 October 2020 419