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Polynomial Representation of the Gaussian Process

Autor: Jesper Kristensen, Liping Wang, Isaac Asher

Publikováno v: Volume 1B: 38th Computers and Information in Engineering Conference.

Gaussian Process (GP) regression is a well-established probabilistic meta-modeling and data analysis tool. The posterior distribution of the GP parameters can be estimated using, e.g., Markov Chain Monte Carlo (MCMC). The ability to make predictions

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2fb1865234e8589fae418a4f7b7f4ce8
https://doi.org/10.1115/detc2018-85145

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A Gaussian Process Modeling Approach for Fast Robust Design With Uncertain Inputs

Autor: You Ling, Liping Wang, Jesper Kristensen, Kevin Ryan, Isaac Asher, Sayan Ghosh

Publikováno v: Volume 7A: Structures and Dynamics.

Many engineering design and industrial manufacturing applications are tasked with finding optimum designs while dealing with uncertainty in the design parameters. The performance or quality of the design may be sensitive to the input variation, makin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::695d42fe5e7f78077569291ec63fdd5e
https://doi.org/10.1115/gt2018-77007

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