Surrogate Modeling For Efficiently, Accurately and Conservatively Estimating Measures of Risk.
Abstract not provided.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- DOE Contract Number:
- NA0003525
- OSTI ID:
- 1889571
- Report Number(s):
- SAND2021-11922C; 700200
- Resource Relation:
- Conference: Proposed for presentation at the Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology held September 26-29, 2021 in San Diego, CA.
- Country of Publication:
- United States
- Language:
- English
Similar Records
Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk
Surrogate Modeling For Efficiently Accurately and Conservatively Estimating Measures of Risk
Nonlinear Regression for Accurate and Efficient Surrogate Models of Materials.
Journal Article
·
2022
· Reliability Engineering and System Safety
·
OSTI ID:1845389
Surrogate Modeling For Efficiently Accurately and Conservatively Estimating Measures of Risk
Technical Report
·
2021
·
OSTI ID:1807455
Nonlinear Regression for Accurate and Efficient Surrogate Models of Materials.
Conference
·
2021
·
OSTI ID:1859842
+1 more