کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
519562 867672 2016 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Gaussian process surrogates for failure detection: A Bayesian experimental design approach
ترجمه فارسی عنوان
جایگزینی فرایندهای گاوسی برای تشخیص شکست: یک روش طراحی تجربی بیزی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی

An important task of uncertainty quantification is to identify the probability of undesired events, in particular, system failures, caused by various sources of uncertainties. In this work we consider the construction of Gaussian process surrogates for failure detection and failure probability estimation. In particular, we consider the situation that the underlying computer models are extremely expensive, and in this setting, determining the sampling points in the state space is of essential importance. We formulate the problem as an optimal experimental design for Bayesian inferences of the limit state (i.e., the failure boundary) and propose an efficient numerical scheme to solve the resulting optimization problem. In particular, the proposed limit-state inference method is capable of determining multiple sampling points at a time, and thus it is well suited for problems where multiple computer simulations can be performed in parallel. The accuracy and performance of the proposed method is demonstrated by both academic and practical examples.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Computational Physics - Volume 313, 15 May 2016, Pages 247–259
نویسندگان
, , ,