Zobrazeno 1 - 10
of 117
pro vyhledávání: '"Howard C. Elman"'
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 3, p 035015 (2023)
We consider the simulation of Bayesian statistical inverse problems governed by large-scale linear and nonlinear partial differential equations (PDEs). Markov chain Monte Carlo (MCMC) algorithms are standard techniques to solve such problems. However
Externí odkaz:
https://doaj.org/article/bc972033eaa543b49ddde88f22266e17
The authors'intended audience is at the level of graduate students and researchers, and we believe that the text offers a valuable contribution to all finite element researchers who would like to broadened both their fundamental and applied knowledge
Publikováno v:
BIT Numerical Mathematics. 62:965-994
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::223735abcb170ba5f634bc97a4718486
https://doi.org/10.1007/s10543-021-00903-x
https://doi.org/10.1007/s10543-021-00903-x
Publikováno v:
Journal of Computational Physics. 448:110699
In magnetic confinement fusion devices, the equilibrium configuration of a plasma is determined by the balance between the hydrostatic pressure in the fluid and the magnetic forces generated by an array of external coils and the plasma itself. The lo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::132536d35ca22a5134edae88721963de
https://doi.org/10.1016/j.jcp.2021.110699
https://doi.org/10.1016/j.jcp.2021.110699
Autor:
Howard C. Elman, Heyrim Cho
Publikováno v:
International Journal for Uncertainty Quantification. 8:193-210
The combination of reduced basis and collocation methods enables efficient and accurate evaluation of the solutions to parameterized PDEs. In this paper, we study the stochastic collocation methods that can be combined with reduced basis methods to s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::afe50f6ad5ed33623c6b65b945f42922
https://doi.org/10.1615/int.j.uncertaintyquantification.2018024436
https://doi.org/10.1615/int.j.uncertaintyquantification.2018024436
Autor:
Howard C. Elman, Virginia Forstall
Publikováno v:
Computer Methods in Applied Mechanics and Engineering. 317:380-399
Reduced-order modeling is an efficient approach for solving parameterized discrete partial differential equations when the solution is needed at many parameter values. An offline step approximates the solution space and an online step utilizes this a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f24ecf70832b240b9ad3cb26e020ba7b
https://doi.org/10.1016/j.cma.2016.12.011
https://doi.org/10.1016/j.cma.2016.12.011
Publikováno v:
SIAM Journal on Scientific Computing. 38:B1009-B1031
The scalable iterative solution of strongly coupled three-dimensional incompressible resistive magnetohydrodynamics (MHD) equations is very challenging because disparate time scales arise from the electromagnetics, the hydrodynamics, as well as the c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::77dfb1ce75092e96132ad73da66df2de
https://doi.org/10.1137/16m1074084
https://doi.org/10.1137/16m1074084
Autor:
Tengfei Su, Howard C. Elman
Publikováno v:
Computer Methods in Applied Mechanics and Engineering. 364:112948
We study a low-rank iterative solver for the unsteady Navier–Stokes equations for incompressible flows with a stochastic viscosity. The equations are discretized using the stochastic Galerkin method, and we consider an all-at-once formulation where
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee4a499a528ee8f28b95696bc459ece2
https://doi.org/10.1016/j.cma.2020.112948
https://doi.org/10.1016/j.cma.2020.112948
Autor:
Howard C. Elman, Quan M. Bui
Simulating compositional multiphase flow in porous media is a challenging task, especially when phase transition is taken into account. The main problem with phase transition stems from the inconsistency of the primary variables such as phase pressur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8cd103f20c815e4f2e60c55768f65ba7
http://arxiv.org/abs/1805.05801
http://arxiv.org/abs/1805.05801