首页 | 本学科首页   官方微博 | 高级检索  
     


Optimal representation of multi-dimensional random fields with a moderate number of samples: Application to stochastic mechanics
Affiliation:1. Department of Civil and Environmental Engineering, ATLSS Engineering Research Center, Lehigh University, 117 ATLSS Drive, Bethlehem, PA 18015-4729, USA;2. Department of Engineering, 200D Weed Hall, Hofstra University, Hempstead, NY 11549-1330, USA;1. Department of Civil & Environmental Engineering, University of Massachusetts, 130 Natural Resources Road, Amherst, MA 01003, USA;2. Department of Civil Engineering & Engineering Mechanics, Columbia University, USA;3. Multifunctional Materials Branch, US Naval Research Laboratory, USA;1. College of Engineering, Huazhong Agricultural University, Wuhan, PR China;2. The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, PR China;1. The Simon A Levin Mathematical, Computational and Modeling Sciences Center Mathematical, Computational and Modeling Sciences Center, and Arizona State University, Tempe, AZ, USA;2. Biodesign Institute, and Arizona State University, Tempe, AZ, USA;3. School of Life Sciences, and Arizona State University, Tempe, AZ, USA;4. EcoHealth Alliance, New York, NY 10001, USA;5. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA;6. Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA;7. Complex Adaptive Systems Initiative, Arizona State University, Scottsdale, AZ, USA;1. SEMTE, Faculties of Mechanical and Aerospace Engineering, Arizona State University, Tempe, AZ 85287-6106, USA;2. Gyeongsang National University, Department of Energy and Mechanical Engineering, Institute of Marine Industry, Tongyoung, Gyeongnam 650-160, South Korea;3. Laboratoire Modélisation et Simulation Multi Echelle, Université Paris-Est, 5, Bd Descartes, 77454 Marne-la-Vallée Cedex 02, France
Abstract:A significant amount of problems and applications in stochastic mechanics and engineering involve multi-dimensional random functions. The probabilistic analysis of these problems is usually computationally very expensive if a brute-force Monte Carlo method is used. Thus, a technique for the optimal selection of a moderate number of samples effectively representing the entire space of sample realizations is of paramount importance. Functional Quantization is a novel technique that has been proven to provide optimal approximations of random functions using a predetermined number of representative samples. The methodology is very easy to implement and it has been shown to work effectively for stationary and non-stationary one-dimensional random functions. This paper discusses the application of the Functional Quantization approach to the domain of multi-dimensional random functions and the applicability is demonstrated for the case of a 2D non-Gaussian field and a two-dimensional panel with uncertain Young modulus under plane stress.
Keywords:Functional Quantization  Multi-dimensional random fields  Monte Carlo simulation  Optimal sampling  Random material properties
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号