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Improving Multi-Model Trajectory Simulation Estimators using Model Selection and Tuning

AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022

Bomarito, Geoffrey F.; Geraci, Gianluca; Warner, James E.; Leser, Patrick E.; Leser, William P.; Eldred, Michael S.; Jakeman, John D.; Gorodetsky, Alex A.

Multi-model Monte Carlo methods have been illustrated to be an efficient and accurate alternative to standard Monte Carlo (MC) in the model-based propagation of uncertainty in entry, descent, and landing (EDL) applications. These multi-model MC methods fuse predictions from low-fidelity models with the high-fidelity EDL model of interest to produce unbiased statistics with a fraction of the computational cost. The accuracy and efficiency of the multi-model MC methods are dependent upon the magnitude of correlations of the low-fidelity models with the high-fidelity model, but also upon the correlation amongst the low-fidelity models, and their relative computational cost. Because of this layer of complexity, the question of how to optimally select the set of low-fidelity models has remained open. In this work, methods for optimal model construction and tuning are investigated as a means to increase the speed and precision of trajectory simulation for EDL. Specifically, the focus is on the inclusion of low-fidelity model tuning within the sample allocation optimization that accompanies multi-model MC methods. Results indicate that low-fidelity model tuning can significantly improve efficiency and precision of trajectory simulations and provide an increased edge to multi-model MC methods when compared to standard MC.

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TYPE Conference Proceeding YEAR 2022
DOIOSTIScopus

Improving Multi-Model Trajectory Simulation Estimators using Model Selection and Tuning

AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022

Bomarito, Geoffrey F.; Geraci, Gianluca; Warner, James E.; Leser, Patrick E.; Leser, William P.; Eldred, Michael S.; Jakeman, John D.; Gorodetsky, Alex A.

Multi-model Monte Carlo methods have been illustrated to be an efficient and accurate alternative to standard Monte Carlo (MC) in the model-based propagation of uncertainty in entry, descent, and landing (EDL) applications. These multi-model MC methods fuse predictions from low-fidelity models with the high-fidelity EDL model of interest to produce unbiased statistics with a fraction of the computational cost. The accuracy and efficiency of the multi-model MC methods are dependent upon the magnitude of correlations of the low-fidelity models with the high-fidelity model, but also upon the correlation amongst the low-fidelity models, and their relative computational cost. Because of this layer of complexity, the question of how to optimally select the set of low-fidelity models has remained open. In this work, methods for optimal model construction and tuning are investigated as a means to increase the speed and precision of trajectory simulation for EDL. Specifically, the focus is on the inclusion of low-fidelity model tuning within the sample allocation optimization that accompanies multi-model MC methods. Results indicate that low-fidelity model tuning can significantly improve efficiency and precision of trajectory simulations and provide an increased edge to multi-model MC methods when compared to standard MC.

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TYPE Conference Presentation YEAR 2022
DOIOSTIScopus

Science and Engineering of Cybersecurity by Uncertainty quantification and Rigorous Experimentation (SECURE) (Final Report)

Pinar, Ali P.; Tarman, Thomas D.; Swiler, Laura P.; Gearhart, Jared L.; Hart, Derek; Vugrin, Eric; Cruz, Gerardo J.; Arguello, Bryan; Geraci, Gianluca; Debusschere, Bert J.; Hanson, Seth T.; Outkin, Alexander V.; Thorpe, Jamie E.; Hart, William E.; Sahakian, Meghan A.; Gabert, Kasimir G.; Glatter, Casey; Johnson, Emma S.; Punla-Green, She'Ifa'

This report summarizes the activities performed as part of the Science and Engineering of Cybersecurity by Uncertainty quantification and Rigorous Experimentation (SECURE) Grand Challenge LDRD project. We provide an overview of the research done in this project, including work on cyber emulation, uncertainty quantification, and optimization. We present examples of integrated analyses performed on two case studies: a network scanning/detection study and a malware command and control study. We highlight the importance of experimental workflows and list references of papers and presentations developed under this project. We outline lessons learned and suggestions for future work.

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TYPE SAND Report YEAR 2021
DOIOSTI

Science & Engineering of Cyber Security by Uncertainty Quantification and Rigorous Experimentation (SECURE) HANDBOOK

Pinar, Ali P.; Tarman, Thomas D.; Swiler, Laura P.; Gearhart, Jared L.; Hart, Derek; Vugrin, Eric; Cruz, Gerardo J.; Arguello, Bryan; Geraci, Gianluca; Debusschere, Bert J.; Hanson, Seth T.; Outkin, Alexander V.; Thorpe, Jamie E.; Hart, William E.; Sahakian, Meghan A.; Gabert, Kasimir G.; Glatter, Casey; Johnson, Emma S.; Punla-Green, and She?Ifa S.

Abstract not provided.

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TYPE Other Report YEAR 2021
DOIOSTI
Results 51–75 of 169
Results 51–75 of 169
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