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Your search keyword '"Kyle Neal"' showing total 3 results
Start Over Author "Kyle Neal" Topic computer science
3 results on '"Kyle Neal"'

1. ROBUST IMPORTANCE SAMPLING FOR BAYESIAN MODEL CALIBRATION WITH SPATIOTEMPORAL DATA

Author

Sankaran Mahadevan, Kyle Neal, Abhinav Subramanian, Joshua Mullins, and Benjamin B. Schroeder

Subjects
Statistics and Probability, Control and Optimization, Computer science, Calibration (statistics), business.industry, Modeling and Simulation, Genetic algorithm, Discrete Mathematics and Combinatorics, Pattern recognition, Artificial intelligence, Bayesian inference, business, Importance sampling
Published
2021
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2. Model Validation and Uncertainty Quantification, Volume 3 : Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics 2023

Author

Roland Platz, Garrison Flynn, Kyle Neal, Scott Ouellette, Roland Platz, Garrison Flynn, Kyle Neal, and Scott Ouellette

Subjects
Engineering mathematics, Engineering—Data processing, Computer science, Civil engineering, Model theory, Mathematical statistics—Data processing
Abstract

Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, the third volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and Uncertainty Quantification, including papers on:Introduction of Uncertainty QuantificationUncertainty Quantification in DynamicsModel Form Uncertainty and Selection incl. Round Robin ChallengeSensor and Information FusionVirtual Sensing, Certification, and Real-Time MonitoringSurrogate Modeling

Published
2023

3. Confidence in the Prediction of Unmeasured System Output Using Roll-Up Methodology

Author

Benjamin B. Schroeder, Joshua Mullins, Kyle Neal, Sankaran Mahadevan, Zhen Hu, Chenzhao Li, and Abhinav Subramanian

Subjects
Estimation theory, Computer science, Calibration (statistics), Bayesian probability, Experimental data, Data mining, Uncertainty quantification, computer.software_genre, computer, System model, Test data, Test (assessment)
Abstract

This research is concerned with how to use available experimental data from tests of lower complexity to inform the prediction regarding a complicated system where no test data is available. Typically, simpler test configurations are used to infer the unknown parameters of an engineering system. Then the calibration results are propagated through the system model to predict the uncertainty in the system response. However, it is important to note that parameter estimation results are affected by the quality of the model used to represent the test configuration. Therefore, it is necessary that the model of the test configuration be also subjected to rigorous validation testing. Then the calibration and validation results for the test configurations need to be integrated to produce the distributions of the parameters to be used in the system-level prediction. Such a systematic roll-up methodology that integrates calibration and validation results at multiple levels of test configurations has been previously established (Sankararaman and Mahadevan, Reliab Eng Syst Saf 138:194–209, 2015).

Published
2018
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