کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6931142 | 867663 | 2015 | 18 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A new algorithm for high-dimensional uncertainty quantification based on dimension-adaptive sparse grid approximation and reduced basis methods
ترجمه فارسی عنوان
یک الگوریتم جدید برای اندازه گیری عدم قطعیت با ابعاد بزرگ بر اساس تقریب شبکه کمیاب سازگار با ابعاد و روش های پایه کاهش یافته
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
In this work we develop an adaptive and reduced computational algorithm based on dimension-adaptive sparse grid approximation and reduced basis methods for solving high-dimensional uncertainty quantification (UQ) problems. In order to tackle the computational challenge of “curse of dimensionality” commonly faced by these problems, we employ a dimension-adaptive tensor-product algorithm [16] and propose a verified version to enable effective removal of the stagnation phenomenon besides automatically detecting the importance and interaction of different dimensions. To reduce the heavy computational cost of UQ problems modelled by partial differential equations (PDE), we adopt a weighted reduced basis method [7] and develop an adaptive greedy algorithm in combination with the previous verified algorithm for efficient construction of an accurate reduced basis approximation. The efficiency and accuracy of the proposed algorithm are demonstrated by several numerical experiments.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Computational Physics - Volume 298, 1 October 2015, Pages 176-193
Journal: Journal of Computational Physics - Volume 298, 1 October 2015, Pages 176-193
نویسندگان
Peng Chen, Alfio Quarteroni,