首页> 外文期刊>Computer Methods in Applied Mechanics and Engineering >Error estimation of the parametric non-intrusive reduced order model using machine learning
【24h】

Error estimation of the parametric non-intrusive reduced order model using machine learning

机译:基于机器学习的参数化非侵入式降阶模型的误差估计

获取原文
获取原文并翻译 | 示例

摘要

A novel error estimation method for the parametric non-intrusive reduced order model (P-NIROM) based on machine learning is presented. This method relies on constructing a set of response functions for the errors between the high fidelity full model solutions and P-NIROM using machine learning method, particularly, Gaussian process regression method. This yields closer solutions agreement with the high fidelity full model. The novelty of this work is that it is the first time to use machine learning method to derive error estimate for the P-NIROM. The capability of the new error estimation method is demonstrated using three numerical simulation examples: flow past a cylinder, dam break and 3D fluvial channel. It is shown that the results are closer to those of the high fidelity full model when considering error terms. In addition, the interface between two phases of dam break case is captured well if the error estimator is involved in the P-NIROM. Crown Copyright (C) 2019 Published by Elsevier B.V. All rights reserved.
机译:提出了一种新的基于机器学习的参数化非侵入式降阶模型误差估计方法。该方法依赖于使用机器学习方法(尤其是高斯过程回归方法)为高保真全模型解决方案与P-NIROM之间的误差构建一组响应函数。这与高保真完整模型产生了更紧密的解决方案协议。这项工作的新颖之处在于,这是第一次使用机器学习方法来为P-NIROM导出误差估计。使用三个数值模拟示例演示了新的误差估计方法的功能:流过圆柱体的水流,溃坝和3D河道。结果表明,考虑误差项时,结果更接近于高保真完整模型。此外,如果P-NIROM中包含误差估计器,则可以很好地捕获水坝破坏情况的两个阶段之间的接口。官方版权(C)2019由Elsevier B.V.保留所有权利。

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