Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Hillary R. Fairbanks"'
Publikováno v:
Numerical Linear Algebra with Applications. 28
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6c9ee06cf862acddff8dea9c7d12c0e7
https://doi.org/10.1002/nla.2352
https://doi.org/10.1002/nla.2352
Publikováno v:
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Universitat Politècnica de Catalunya (UPC)
Particle-laden turbulent flows subject to radiative heating are relevant in many applications, for example concentrated solar power receivers. Efficient and accurate simulations provide valuable insights and enable optimization of such systems. Howev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18b0fcf12edd93fd99625209ad2ea532
https://hdl.handle.net/2117/187290
https://hdl.handle.net/2117/187290
In this work we develop a new hierarchical multilevel approach to generate Gaussian random field realizations in an algorithmically scalable manner that is well-suited to incorporate into multilevel Markov chain Monte Carlo (MCMC) algorithms. This ap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3fd0f16fa8fc413bcf9740c9d4c17108
Publikováno v:
Journal of Computational Physics. 368:315-332
For practical model-based demands, such as design space exploration and uncertainty quantification (UQ), a high-fidelity model that produces accurate outputs often has high computational cost, while a low-fidelity model with less accurate outputs has
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::64ffed31eeb6a06bfa24200407f40509
https://doi.org/10.1016/j.jcp.2018.04.015
https://doi.org/10.1016/j.jcp.2018.04.015
Autor:
Elizabeth Beer, Priya Mani, Jessica Ruth Metcalf-Burton, Gulce Bal, Carlotta Domeniconi, Hillary R. Fairbanks, Marilyn Vazquez, Sibel Tari
Publikováno v:
Association for Women in Mathematics Series ISBN: 9783030115654
High-dimensional data analysis is often negatively affected by the curse of dimensionality. In high-dimensional spaces, data becomes extremely sparse and distances between points become indistinguishable. As a consequence, reliable estimations of den
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8d2d93032f090e855c64396268f4371f
https://doi.org/10.1007/978-3-030-11566-1_2
https://doi.org/10.1007/978-3-030-11566-1_2
Publikováno v:
AIP Conference Proceedings.
The study of complex systems is often based on computationally intensive, high-fidelity, simulations. To build confidence or improve the prediction accuracy of such simulations, the impact of uncertainties in model inputs, or even the structure of th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9c8349225b995e2aef9b11433f49dc72
https://doi.org/10.1063/1.5044952
https://doi.org/10.1063/1.5044952
Multilevel Monte Carlo (MLMC) is a recently proposed variation of Monte Carlo (MC) simulation that achieves variance reduction by simulating the governing equations on a series of spatial (or temporal) grids with increasing resolution. Instead of dir
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1cf48dca7a7b0b26d909c8fa53e66804
http://arxiv.org/abs/1611.02213
http://arxiv.org/abs/1611.02213