Shankar Sankararaman, Ph.D.

Affiliations: 
2012 Vanderbilt University, Nashville, TN 
Area:
Civil Engineering, Statistics, Mechanical Engineering
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Sankaran Mahadevan grad student 2012 Vanderbilt
 (Uncertainty quantification and integration in engineering systems.)
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Publications

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Khorasgani H, Biswas G, Sankararaman S. (2016) Methodologies for system-level remaining useful life prediction Reliability Engineering and System Safety. 154: 8-18
Sankararaman S. (2016) Uncertainty reduction using Bayesian inference and sensitivity analysis: A sequential approach to the NASA langley uncertainty quantification challenge 18th Aiaa Non-Deterministic Approaches Conference
Chiachío J, Sankararaman S, Chiachío M, et al. (2015) Prognostics design for structural health management Emerging Design Solutions in Structural Health Monitoring Systems. 234-273
Sankararaman S, Ling Y, Mahadevan S. (2015) Fatigue crack growth analysis and damage prognosis in structures Emerging Design Solutions in Structural Health Monitoring Systems. 207-233
Sankararaman S, Mahadevan S. (2015) Statistical approach to structural damage diagnosis under uncertainty Emerging Design Solutions in Structural Health Monitoring Systems. 153-169
Sankararaman S. (2015) Sequential refinement of uncertainty through Bayesian inference and global sensitivity analysis Journal of Aerospace Information Systems. 12: 49-72
Liang C, Mahadevan S, Sankararaman S. (2015) Stochastic multidisciplinary analysis under epistemic uncertainty Journal of Mechanical Design, Transactions of the Asme. 137
Daigle M, Sankararaman S, Kulkarni CS. (2015) Stochastic prediction of remaining driving time and distance for a planetary rover Ieee Aerospace Conference Proceedings. 2015
Sankararaman S, Teubert C. (2015) Impact of uncertainty on the diagnostics and prognostics of a Current-Pressure transducer Ieee Aerospace Conference Proceedings. 2015
Sankararaman S. (2015) Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction Mechanical Systems and Signal Processing. 52: 228-247
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