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A computationally efficient approach for inverse material characterization combining Gappy POD with direct inversion

机译:结合Gappy POD和直接反演的逆向材料表征的高效计算方法

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摘要

An approach for computationally efficient inverse material characterization from partial-field response measurements that combines the Gappy proper orthogonal decomposition (POD) machine learning technique with a physics-based direct inversion strategy is presented and evaluated. Gappy POD is used to derive a data reconstruction tool from a set of potential system response fields that are generated from available a priori information regarding the potential distribution of the unknown material properties. Then, the Gappy POD technique is applied to reconstruct the full spatial distribution of the system response from whatever portion of the response field has been measured with the chosen system testing method. Lastly, a direct inversion strategy is presented that is derived from the equations governing the system response (i.e., physics of the system), which utilizes the full-field response reconstructed by Gappy POD to produce an estimate of the spatial distribution of the unknown material properties. The direct inversion technique is a particularly computationally efficient inversion technique, requiring a cost equivalent to a single numerical analysis. Therefore, the majority of the computational expense of the presented approach is the one-time potential response generation for the Gappy POD technique, which leads to an approach that is substantially computationally efficient overall. Two numerically simulated examples are shown in which the elastic modulus distribution was characterized based on partial-field displacement response measurements, both static and dynamic. The inversion procedure was shown to have the capability to efficiently provide accurate estimates to material property distributions from partial-field response measurements. The direct inversion with Gappy POD response estimation was also shown to be substantially tolerant to noise in comparison to the direct inversion given measured full-field response. Lastly, a physical example regarding elastography of an arterial construct from ultrasound imaging response measurements is shown to validate the practical applicability of the direct inversion approach with Gappy POD response reconstruction. (C) 2015 Elsevier B.V. All rights reserved.
机译:提出并评估了一种基于局部场响应测量的高效计算逆材料表征方法,该方法将Gappy固有正交分解(POD)机器学习技术与基于物理的直接反演策略相结合。 Gappy POD用于从一组潜在系统响应字段中获取数据重建工具,该系统是根据有关未知材料属性的潜在分布的先验信息生成的。然后,将Gappy POD技术应用于从已使用所选系统测试方法测量的响应字段的任何部分重建系统响应的完整空间分布。最后,提出了一种直接反演策略,该策略是从控制系统响应的方程式(即系统的物理特性)中得出的,该方程利用Gappy POD重建的全场响应来估算未知物质的空间分布属性。直接反演技术是一种在计算上特别有效的反演技术,需要与单次数值分析等效的成本。因此,所提出的方法的大部分计算开销是针对盖普POD技术的一次性潜在响应生成,从而导致该方法总体上在计算上实质上是有效的。显示了两个数值模拟的示例,其中基于部分场位移响应测量(静态和动态)来表征弹性模量分布。结果表明,该反演程序能够有效地根据部分场响应测量结果对材料特性分布进行准确估算。与给定的全场响应给定的直接反演相比,使用盖普POD响应估计的直接反演也显示出对噪声的耐受性。最后,一个关于从超声成像响应测量中对动脉构造进行弹性成像的物理示例显示,可以验证直接反演方法与Gappy POD响应重建的实际适用性。 (C)2015 Elsevier B.V.保留所有权利。

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  • 作者单位

    Univ Pittsburgh, Dept Civil & Environm Engn, Pittsburgh, PA 15261 USA;

    Univ Pittsburgh, Dept Med & Heart, Ctr Ultrasound Mol Imaging & Therapeut, Pittsburgh, PA 15261 USA|Univ Pittsburgh, Vasc Inst, Pittsburgh, PA 15261 USA|Univ Pittsburgh, Med Ctr, Pittsburgh, PA 15261 USA;

    Univ Pittsburgh, Dept Med & Heart, Ctr Ultrasound Mol Imaging & Therapeut, Pittsburgh, PA 15261 USA|Univ Pittsburgh, Vasc Inst, Pittsburgh, PA 15261 USA|Univ Pittsburgh, Med Ctr, Pittsburgh, PA 15261 USA|Univ Pittsburgh, Dept Bioengn, Pittsburgh, PA 15261 USA|Univ Pittsburgh, McGowan Inst Regenerat Med, Pittsburgh, PA 15261 USA;

    Univ Pittsburgh, Dept Civil & Environm Engn, Pittsburgh, PA 15261 USA|Univ Pittsburgh, Dept Bioengn, Pittsburgh, PA 15261 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Gappy proper orthogonal decomposition; Direct inversion; Material characterization; Computational inverse mechanics; Inverse problem;

    机译:Gappy固有正交分解;直接反演;材料表征;计算逆力学;逆问题;

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