Kriging and Conditional Simulation of Gaussian Field

Hoshiya, Masaru

doi:10.1061/(asce)0733-9399(1995)121:2(181)

Cited by 49 publications

(14 citation statements)

References 2 publications

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“…Application of the conditional simulation of ground motion records marked by nonstationary behavior are available in the literature, e.g., Vanmarcke and Fenton (1991), Hoshiya (1995), Shinozuka and Zhang (1996), Kameda and Morikawa (1994) and Heredia-Zavoni and Santa-Cruz (2000). These techniques are based on Kriging, conditional probability density function or their combination.…”

Section: Non-stationary Processesmentioning

confidence: 99%

“…Lumley (1970) and Armitt (1968) introduced this technique to address turbulence and wind-related problems, respectively, and it was later used by many researchers in describing pressure fluctuations on buildings and structures and a host of wind-related problems (e.g., Lee 1975; Kareem and Cermak 1984;Holmes 1992;Kareem 1999;Tamura et al 1999;Carassale et al 2001). In stochastic structural mechanics, the POD technique based on the covariance matrix has been utilized for the simulation of spatially varying correlated random variables (e.g., Yamazaki and Shinozuka 1990), stochastic finite element analysis (e.g., Ghanem and Spanos 1991), and stochastic dynamics (Li and Kareem 1989, 1995, Vasta and Schueller 2000.…”

Section: Proper Orthogonal Decompositionmentioning

confidence: 99%

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Numerical simulation of wind effects: A probabilistic perspective

Kareem

2008

Journal of Wind Engineering and Industrial Aerodynamics

127

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Numerical simulations of wind loads and their effects are critical in the design of structures to ensure their safety under winds. The simulations range from generation of time histories of wind velocity, pressure and force fluctuations to structural response and assessment of attendant functionality and safety under service and design loads, respectively. Typically these schemes employ Monte Carlo based approaches that encompass model based simulations or information derived from observed data. The scope of simulations spans uni-variate to multivariate processes; uni-dimensional to multi-dimensional fields; Gaussian to non-Gaussian; stationary to non-stationary; conditional and unconditional cases. In order to accomplish these tasks, methods based on the time, frequency, or time-frequency domains are employed. This paper summarizes a historical perspective, recent developments and future challenges. Also included in the discussion are computational tools employed for data analysis, response analysis and its management, and prediction. Examples are presented to illustrate some of the topics discussed.

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Section: Non-stationary Processesmentioning

confidence: 99%

Section: Proper Orthogonal Decompositionmentioning

confidence: 99%

Numerical simulation of wind effects: A probabilistic perspective

Kareem

2008

Journal of Wind Engineering and Industrial Aerodynamics

127

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“…They can be generated using a variety of methods, from matrix decomposition to spectral methods, as presented in [28,29,30,31]. Examples of such conditional simulations are displayed in Figure 1b.…”

Section: Conditional Simulationsmentioning

confidence: 99%

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations

Binois

Ginsbourger

Roustant

2015

European Journal of Operational Research

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Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill sampling criteria balancing exploitation and exploration such as the Expected Hypervolume Improvement. Here we consider Kriging metamodels not only for selecting new points, but as a tool for estimating the whole Pareto Front and quantifying how much uncertainty remains on it at any stage of Kriging-based multi-objective optimization algorithms. Our approach relies on the Gaussian Process interpretation of Kriging, and bases upon conditional simulations. Using concepts from random set theory, we propose to adapt the Vorob'ev expectation and deviation to capture the variability of the set of non-dominated points. Numerical experiments illustrate the potential of the proposed workflow, and it is shown on examples how Gaussian process simulations and the estimated Vorob'ev deviation can be used to monitor the ability of Kriging-based multi-objective optimization algorithms to accurately learn the Pareto front.

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“…The theoretical model of choice depended on the specific cases. According to the studies of predecessors, theoretical variogram model of soil properties were generally the spherical model of transition (Masaru Hoshiya, 1995;Gotway C A. R B Ferguson,1996). When selecting the semi-variance model, first of all, used the equation (1) …”

Section: Model Choicementioning

confidence: 99%

Reasonable Sampling Scale of Macropore Based on Geostatistic Theory

Zhou

Lin

et al. 2009

IFIP Advances in Information and Communication Technology

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Abstract:Spatial scale of soil character is forefront in soil-scientific research, and the analysis of macropore reasonable sampling size becomes significant in studyinig on soil spatial variability. From the micro-structure of the soil, using digital image preparation and analysis technology, this paper described the soil macropore with rations, discussed the spatial structure of soil macropore on both the vertical and horizontal directions by geologic geostatistic theory , analysed by the base value of variation function C0, structure variance C, and so on. The results showed that the reasonable sampling interval of vertical depth was 20 mm, and the economic reasonable sampling diameter on horizontal was about 90 ~ 100 mm. The production has very important significance to ensuring the scientific of the soil macropore research, saving study time, and reducing the human and financial resources in the trial.

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Kriging and Conditional Simulation of Gaussian Field

Cited by 49 publications

References 2 publications

Numerical simulation of wind effects: A probabilistic perspective

Numerical simulation of wind effects: A probabilistic perspective

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations

Reasonable Sampling Scale of Macropore Based on Geostatistic Theory

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