A non-Gaussian stochastic model from limited observations using polynomial chaos and fractional moments

• Published in: Reliability engineering & system safety Vol. 221; p. 108323
• Main Authors: Zhang, Ruijing, Dai, Hongzhe
• Format: Journal Article
• Language: English
• Published: Barking Elsevier Ltd 01.05.2022; Elsevier BV
• Subjects: Bootstrapping; Entropy; Fields (mathematics); Fractional moments; Karhunen-Loève expansion; Limited observations; Maximum entropy; Non-Gaussian model; Personal computers; Polynomial chaos; Polynomials; Probability theory; Random variables; Randomness; Reliability analysis; Reliability engineering; Representations; Statistical analysis; Stochastic models; Stochastic processes; Bootstrapping; Karhunen-Loève expansion; Non-Gaussian model; Limited observations; Fractional moments; Polynomial chaos
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2022.108323

•A unified non-Gaussian stochastic model from limited observations is developed.•The developed model is based on polynomial chaos (PC) and fractional moments.•The developed model can be embedded into PC framework for stochastic propagation.•Two numerical examples demonstrate the developed model. The...