Zobrazeno 1 - 10
of 23
pro vyhledávání: '"M. Giselle Fernández-Godino"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract For centuries, scientists have observed nature to understand the laws that govern the physical world. The traditional process of turning observations into physical understanding is slow. Imperfect models are constructed and tested to explain
Externí odkaz:
https://doaj.org/article/a3352c71d14044c188d68314a5cbc95e
Autor:
Qingkai Kong, Andrea Chiang, Ana C. Aguiar, M. Giselle Fernández-Godino, Stephen C. Myers, Donald D. Lucas
Publikováno v:
Artificial Intelligence in Geosciences, Vol 2, Iss , Pp 96-106 (2021)
The idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing. In this paper, we designed tests to evaluate this idea of using autoencoders as feature extractors for di
Externí odkaz:
https://doaj.org/article/9b87ac8df4934f6182c4c4510467541d
Autor:
Yinan Wang, Diane Oyen, Weihong (Grace) Guo, Anishi Mehta, Cory Braker Scott, Nishant Panda, M. Giselle Fernández-Godino, Gowri Srinivasan, Xiaowei Yue
Publikováno v:
npj Materials Degradation, Vol 5, Iss 1, Pp 1-10 (2021)
Abstract Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal stress is critical to predicting time to failure and improvi
Externí odkaz:
https://doaj.org/article/47873e5969e84597a0ca8f3758f5d4bf
For centuries, scientists have observed nature to understand the laws that govern the physical world. The traditional process of turning observations into physical understanding is slow. Imperfect models are constructed and tested to explain relation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4add4f4e746a5fdc612e7d13ad4a4738
https://doi.org/10.21203/rs.3.rs-1455160/v1
https://doi.org/10.21203/rs.3.rs-1455160/v1
Publikováno v:
Computational Geosciences. 25:233-242
Complex dynamical systems are an integral part of predictive analysis that model diverse phenomena. As these models improve, they become more complex and depend on an increasing number of model or driver inputs. Uncertainty plagues these inputs (init
Publikováno v:
AIAA Journal. 57:2039-2054
Multifidelity surrogates are essential in cases where it is not affordable to have more than a few high-fidelity samples, but it is affordable to have as many low-fidelity samples as needed. In the...
Autor:
Diane Oyen, Xiaowei Yue, Nishant Panda, M. Giselle Fernández-Godino, Anishi Mehta, Weihong Guo, Yinan Wang, Cory B. Scott, Gowri Srinivasan
Publikováno v:
npj Materials Degradation, Vol 5, Iss 1, Pp 1-10 (2021)
Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal stress is critical to predicting time to failure and improving the fr
Autor:
Julia B. Nakhleh, M. Giselle Fernández-Godino, Brandon Wilson, Gowri Srinivasan, John Kline, Michael Grosskopf
A sustainable burn platform through inertial confinement fusion (ICF) has been an ongoing challenge for over 50 years. Mitigating engineering limitations and improving the current design involves an understanding of the complex coupling of physical p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55d048b73d9dbeb6c4aa2b5093fab242
http://arxiv.org/abs/2010.15208
http://arxiv.org/abs/2010.15208
Autor:
John Kline, Michael Grosskopf, Julia B. Nakhleh, Gowri Srinivasan, Brandon Wilson, M. Giselle Fernández-Godino
Building a sustainable burn platform in inertial confinement fusion (ICF) requires an understanding of the complex coupling of physical processes and the effects that key experimental design changes have on implosion performance. While simulation cod
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::888d7eb53c8e051d64dacf142c778e91
http://arxiv.org/abs/2010.04254
http://arxiv.org/abs/2010.04254
Autor:
Diane Oyen, Nishant Panda, Daniel O'Malley, Raphael T. Haftka, Gowri Srinivasan, M. Giselle Fernández-Godino, Kyle S. Hickmann
Publikováno v:
AIAA Scitech 2020 Forum.