• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>SIAM/ASA Journal on Uncertainty Quantification >Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices
【24h】

Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices

机译：Multifidelity蒙特卡罗估计方差和敏感性指数

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Variance-based sensitivity analysis provides a quantitative measure of how uncertainty in a model input contributes to uncertainty in the model output. Such sensitivity analyses arise in a wide variety of applications and are typically computed using Monte Carlo estimation, but the many samples required for Monte Carlo to be suffciently accurate can make these analyses intractable when the model is expensive. This work presents a multifidelity approach for estimating sensitivity indices that leverages cheaper low-fidelity models to reduce the cost of sensitivity analysis while retaining accuracy guarantees via recourse to the original, expensive model. This paper develops new multifidelity estimators for variance and for the Sobol' main and total effect sensitivity indices. We discuss strategies for dividing limited computational resources among models and specify a recommended strategy. Results are presented for the Ishigami function and a convection-diffusionreaction model that demonstrate up to 10 speedups for fixed convergence levels. For the problems tested, the multifidelity approach allows inputs to be definitively ranked in importance when Monte Carlo alone fails to do so.
机译：Variance-based敏感性分析提供了一个在一个定量测量的不确定性模型输入导致的不确定性模型输出。各种各样的应用程序和一般计算使用蒙特卡罗估计,但是许多样品所需的蒙特卡洛可以使这些分析极限准确棘手当模型是昂贵的。礼物multifidelity方法工作估计敏感性指数,利用便宜的低保真模型来降低成本敏感性分析,同时保持精度通过求助于原担保,昂贵的模型。multifidelity估计方差和Sobol的主要敏感指数和总效应。我们讨论策略划分有限计算资源模型和指定推荐策略。Ishigami函数和一个convection-diffusionreaction模型示范10固定的加速效果收敛性的水平。multifidelity方法允许输入蒙特时明确排名的重要性卡洛独自未能这样做。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号