default author photo

Sangkeun Lee

Government of the United States of America - Computer Science and Mathematics Division

1 Bethel Valley Road, P.O. Box 2008, Mail Stop 608

Room B-106, Building 5700

Oak Ridge, TN 37831

United States

SCHOLARLY PAPERS

1

DOWNLOADS

132

TOTAL CITATIONS

0

Scholarly Papers (1)

Actions:
Email selected abstracts
1.

Modern Data Analytics Approach to Predict Creep of High-Temperature Alloys

Number of pages: 48 Posted: 20 Nov 2018
Dongwon Shin, Yukinori Yamamoto, Michael P. Brady, Sangkeun Lee and James Haynes
Government of the United States of America - Materials Science and Technology Division, Government of the United States of America - Materials Science and Technology Division, Government of the United States of America - Materials Science and Technology Division, Government of the United States of America - Computer Science and Mathematics Division and Government of the United States of America - Materials Science and Technology Division
Downloads 132 (551,165)

Abstract:

Loading...

high-temperature alloys, creep, correlation analysis, machine learning, features, computational thermodynamics