Position Paper: Applying Machine Learning to Software Analysis to Achieve Trusted, Repeatable Scientific Computing
Conference
·
OSTI ID:1265800
- ORNL
Producing trusted results from high-performance codes is essential for policy and has significant economic impact. We propose combining rigorous analytical methods with machine learning techniques to achieve the goal of repeatable, trustworthy scientific computing.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1265800
- Resource Relation:
- Conference: ASCR Cybersecurity Workshop 2, Arlington, VA, USA, 20150602, 20150603
- Country of Publication:
- United States
- Language:
- English
Similar Records
Performance Analysis of Scientific Computing Workloads on Trusted Execution Environments
ASCR Cybersecurity for Scientific Computing Integrity - Research Pathways and Ideas Workshop
SAGE Intrusion Detection System: Sensitivity Analysis Guided Explainability for Machine Learning.
Technical Report
·
Sun Oct 25 00:00:00 EDT 2020
·
OSTI ID:1265800
+2 more
ASCR Cybersecurity for Scientific Computing Integrity - Research Pathways and Ideas Workshop
Technical Report
·
Wed Jun 03 00:00:00 EDT 2015
·
OSTI ID:1265800
SAGE Intrusion Detection System: Sensitivity Analysis Guided Explainability for Machine Learning.
Technical Report
·
Wed Sep 01 00:00:00 EDT 2021
·
OSTI ID:1265800
+12 more