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Publication | Type | Year |
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Exploration of multifidelity UQ sampling strategies for computer network applicationsInternational Journal for Uncertainty Quantification |
Journal Article – 2021 Journal Article | 2021 |
Quantifying Uncertainty in Emulations: LDRD Report |
SAND Report – 2019 SAND Report | 2019 |
Uncertainty Quantification in Cyber EmulationInforms |
Conference Paper – 2019 Conference Paper | 2019 |
Lessons Learned from 10k Experiments to Compare Virtual and Physical Testbeds12th USENIX Workshop on Cyber Security Experimentation and Test |
Conference Paper – 2019 Conference Paper | 2019 |
Exploration of multifidelity approaches for uncertainty quantification in network applications3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP2019) |
Conference Paper – 2019 Conference Paper | 2019 |
Exploration Of Multifidelity Approaches For Uncertainty Quantification In Network Applications3rd Eccomas Thematic Conference on Uncertainty Quantification in Computational Science and Engineering |
Conference Paper – 2019 Conference Paper | 2019 |
SECURE Uncertainty Quantification ThrustExternal Advisory Board meeting, SECURE Grand Challenge |
Presentation (non-conference) – 2019 Presentation (non-conference) | 2019 |
Exploration of multifidelity UQ strategies for network applicationsLDRD SECURE Grand Challenge External Advisory Board meeting |
Presentation (non-conference) – 2019 Presentation (non-conference) | 2019 |
Virtually the Same: Comparing Physical and Virtual TestbedsInternational Conference on Computing, Networking and Communications (ICNC 2019) |
Conference Paper – 2019 Conference Paper | 2019 |
Virtually the Same: Comparing Physical and Virtual TestbedsInternational Conference on Computing, Networking and Communication (ICNC) 2019 |
Conference Paper – 2018 Conference Paper | 2018 |
Virtually the Same? The Empirical Differences Between Physical and Virtual NetworksResearch Directions for Cyber Experimentation Workshop |
Conference Paper – 2017 Conference Paper | 2017 |
Machine Learning in the Presence of Adversarial Tampering, or, What To Do When Your Truth Data Lies?ASCR Machine Learning for Scientific Discovery Workshop
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Abstract – 2014 Abstract | 2014 |
Document Title | Type | Year |