Zobrazeno 1 - 10
of 121
pro vyhledávání: '"Dorin, Thomas"'
Recycled aluminum alloys are pivotal for sustainable manufacturing, offering strength, durability, and environmental advantages. However, the presence of iron (Fe) impurities poses a major challenge, undermining their properties and recyclability. Co
Externí odkaz:
http://arxiv.org/abs/2310.06327
Autor:
Hu, Mingwei, Tan, Qiyang, Knibbe, Ruth, Xu, Miao, Liang, Guofang, Zhou, Jianxin, Xu, Jun, Jiang, Bin, Li, Xue, Ramajayam, Mahendra, Dorin, Thomas, Zhang, Ming-Xing
Publikováno v:
In Computational Materials Science September 2024 244
Autor:
Yang, Yi, Wang, Jun, Ferdowsi, Mahmoud Reza Ghandehari, Kada, Sitarama R., Dorin, Thomas, Barnett, Matthew R., Perez, Michel
Publikováno v:
In Acta Materialia 1 December 2024 281
Autor:
Pasco, Jubert, Jiang, Lu, Dorin, Thomas, Keshavarzkermani, Ali, He, Youliang, Aranas, Clodualdo, Jr
Publikováno v:
In Materials Characterization January 2024 207
Autor:
Yang, Yi, Massardier, Veronique, Ferdowsi, Mahmoud Reza Ghandehari, Jiang, Lu, Wang, Jun, Dorin, Thomas, Kada, Sitarama R., Barnett, Matthew R., Perez, Michel
Publikováno v:
In Acta Materialia 1 November 2023 260
Publikováno v:
In Materials Science & Engineering A 10 August 2023 881
Publikováno v:
In Materials Science & Engineering A 6 June 2023 875
Akademický článek
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Autor:
Shilton, Alistair, Gupta, Sunil, Rana, Santu, Vellanki, Pratibha, Park, Laurence, Li, Cheng, Venkatesh, Svetha, Sutti, Alessandra, Rubin, David, Dorin, Thomas, Vahid, Alireza, Height, Murray, Slezak, Teo
Publikováno v:
PMLR 108:635-645, 2020
Bayesian optimization (BO) is a widely-used method for optimizing expensive (to evaluate) problems. At the core of most BO methods is the modeling of the objective function using a Gaussian Process (GP) whose covariance is selected from a set of stan
Externí odkaz:
http://arxiv.org/abs/1805.07852
Autor:
Shilton, Alistair, Gupta, Sunil, Rana, Santu, Vellanki, Pratibha, Li, Cheng, Park, Laurence, Venkatesh, Svetha, Sutti, Alessandra, Rubin, David, Dorin, Thomas, Vahid, Alireza, Height, Murray
The paper presents a novel approach to direct covariance function learning for Bayesian optimisation, with particular emphasis on experimental design problems where an existing corpus of condensed knowledge is present. The method presented borrows te
Externí odkaz:
http://arxiv.org/abs/1802.05370