Zobrazeno 1 - 10
of 136
pro vyhledávání: '"Yong-Jin Han"'
Autor:
Xiaoting Zhong, Brian Gallagher, Shusen Liu, Bhavya Kailkhura, Anna Hiszpanski, T. Yong-Jin Han
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-19 (2022)
Abstract Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explainable artifi
Externí odkaz:
https://doaj.org/article/599f9fa1c28a451e9cc5dc0278c44c89
Autor:
Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, Xiaoting Zhong, T. Yong-Jin Han
Publikováno v:
ACS Omega, Vol 7, Iss 3, Pp 2624-2637 (2022)
Externí odkaz:
https://doaj.org/article/b3746167135a4ba89a10291ecb901fa8
Autor:
Xiaoting Zhong, Brian Gallagher, Keenan Eves, Emily Robertson, T. Nathan Mundhenk, T. Yong-Jin Han
Publikováno v:
npj Computational Materials, Vol 7, Iss 1, Pp 1-11 (2021)
Abstract Machine-learning (ML) techniques hold the potential of enabling efficient quantitative micrograph analysis, but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated. We collect
Externí odkaz:
https://doaj.org/article/15813301f7d54bfcb75f7018545b366d
Publikováno v:
ACS Omega, Vol 6, Iss 19, Pp 12711-12721 (2021)
Externí odkaz:
https://doaj.org/article/345193fec2db4d929f5aed5fe19d3fff
Autor:
Brian Gallagher, Matthew Rever, Donald Loveland, T. Nathan Mundhenk, Brock Beauchamp, Emily Robertson, Golam G. Jaman, Anna M. Hiszpanski, T. Yong-Jin Han
Publikováno v:
Materials & Design, Vol 190, Iss , Pp - (2020)
We explore the application of computer vision and machine learning (ML) techniques to predict material properties (e.g., compressive strength) based on SEM images. We show that it's possible to train ML models to predict materials performance based o
Externí odkaz:
https://doaj.org/article/b0c0c231d88d41bd920defda87945393
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Hyun-Chul Lee, Yong-Jin Han
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 77, Iss 10, Pp 1-9 (2017)
Abstract We study the innermost stable circular orbit (ISCO) of the metric of the Kerr black hole in modified gravity (Kerr-MOG black hole), which is one of the exact solutions of the field equation of modified gravity in the strong gravity regime. T
Externí odkaz:
https://doaj.org/article/39898172554d42f3bf6abc59c2e264b1
Publikováno v:
Chemistry of Materials. 34:7650-7665
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::28b20ec1f502a77012f00e1f4b6e32eb
https://doi.org/10.1021/acs.chemmater.2c01333
https://doi.org/10.1021/acs.chemmater.2c01333
Autor:
Choi, Jiwoo, Bang, Kihoon, Jang, Suji, Choi, Jaewoong, Ordonez, Juanita, Buttler, David, Hiszpanski, Anna, Yong-Jin Han, T., Sohn, Seok Su, Lee, Byungju, Lee, Kwang-Ryeol, Han, Sang Soo, Kim, Donghun
Publikováno v:
Journal of Materials Chemistry A; 9/7/2023, Vol. 11 Issue 33, p17628-17643, 16p
Publikováno v:
ACS Omega
ACS Omega, Vol 6, Iss 19, Pp 12711-12721 (2021)
ACS Omega, Vol 6, Iss 19, Pp 12711-12721 (2021)
In this paper, we leverage predictive uncertainty of deep neural networks to answer challenging questions material scientists usually encounter in machine learning-based material application workflows. First, we show that by leveraging predictive unc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::191f091988ddc02120902a0f6e5542b7
https://doi.org/10.1021/acsomega.1c00975
https://doi.org/10.1021/acsomega.1c00975