Uncertainty quantification and reliability analysis by an adaptive sparse Bayesian inference based PCE model
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,General Engineering,Modeling and Simulation,Software
Link
http://link.springer.com/content/pdf/10.1007/s00366-021-01291-0.pdf
Reference64 articles.
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2. Abraham S, Tsirikoglou P, Miranda J, Lacor C, Contino F, Ghorbaniasl G (2018) Spectral representation of stochastic field data using sparse polynomial chaos expansions. J Comput Phys 367:109–120
3. Au SK, Beck JL (2001) Estimation of small failure probabilities in high dimensions by subset simulation. Probab Eng Mech 16(4):263–277
4. Bhattacharyya B (2018) A critical appraisal of design of experiments for uncertainty quantification. Arch Comput Methods Eng 25(3):727–751
5. Bhattacharyya B (2020) Global sensitivity analysis: a Bayesian learning based polynomial chaos approach. J Comput Phys 415(109539):1–22
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