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Dynamic Properties of Elastic Media Obtained with Self-Trained Convolutional Neural Networks

自训练卷积神经网络获得弹性介质的动态特性

基本信息

批准号：
2054768
负责人：
Bogdan-Ioan Popa
金额：
$ 35.61万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2054768&HistoricalAwards=false
关键词：
Dynamic Properties Elastic Media Obtained

项目摘要

This research will derive new knowledge related to the non-destructive evaluation of all the mechanical parameters that fully describe the elastic behavior of complex media such as biological tissue and designer materials with carefully engineered microstructure. Previous methods for non-destructive evaluation often rely on limiting assumptions on the nature of the probed material (for example, direction-independent properties), require large samples for evaluation, and/or provide only a small subset of material parameters. For example, accurately evaluating all the mechanical parameters of biological tissue is important because it informs on tissue changes due to pathological processes. However, state of the art methods used to probe these parameters (e.g. elastography) may provide only a small set of mechanical parameters, which may delay the detection of tissue pathologies. This project will create new methods to extract the complete set of material properties from scattered ultrasound pulses processed with convolutional neural networks trained in an unsupervised manner. The results of this research will be leveraged by numerous fields ranging from infrastructure integrity evaluation to non-invasive medical diagnostics and thus will benefit the society at multiple levels. This research will also provide the opportunity to develop an online wave dynamics lab accessed and controlled remotely, which will increase the participation of economically disadvantaged students and underrepresented minorities to cutting-edge experimental science.Analytical methods currently used to extract the dynamic mechanical properties of matter from scattered mechanical waves have proven insufficient when several dozen parameters are unknown. This effort will derive new knowledge and tools necessary to extract all the unknown parameters of complex media with anisotropic stiffness, mass density, and Willis parameter tensors. The central hypothesis is that convolutional neural networks can learn the very complex mapping scattered-fields-to-constitutive-parameters from numerical simulations. This simulation-based approach will lead to an unsupervised self-training process that contrasts with most machine learning applications requiring expensive training data sets typically labeled by humans and obtained in long measurement sessions. This hypothesis will be verified by fabricating and extracting the material parameters of elastic metamaterials designed to have anisotropic stiffness, mass density, and Willis parameter tensors. This research will consider material property extraction from both near- and far-field measurements.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项研究将获得与所有力学参数的无损评估相关的新知识，这些参数完全描述了复杂介质的弹性行为，如生物组织和具有精心设计的微结构的设计材料。以前的无损评估方法通常依赖于对被探测材料的性质(例如，与方向无关的属性)的限制假设，需要用于评估的大样本，和/或仅提供材料参数的一小部分。例如，准确评估生物组织的所有力学参数是很重要的，因为它可以告知组织由于病理过程而发生的变化。然而，用于探测这些参数的现有方法(例如，弹性成像)可能只提供一小组机械参数，这可能会延迟组织病理学的检测。该项目将创造新的方法，从以无监督方式训练的卷积神经网络处理的散射超声脉冲中提取完整的材料属性集。这项研究的结果将被许多领域利用，从基础设施完整性评估到非侵入性医疗诊断，从而将在多个层面上造福社会。这项研究还将提供开发远程访问和控制的在线波浪动力学实验室的机会，这将使经济困难的学生和代表性不足的少数群体更多地参与尖端实验科学。目前用于从分散的机械波中提取物质动态力学性质的分析方法已被证明在数十个参数未知的情况下是不够的。这一努力将获得提取具有各向异性刚度、质量密度和Willis参数张量的复杂介质的所有未知参数所需的新知识和工具。中心假设是卷积神经网络可以从数值模拟中学习非常复杂的从散射场到本构参数的映射。这种基于模拟的方法将导致无监督的自我训练过程，这与大多数机器学习应用程序不同，大多数机器学习应用程序需要昂贵的训练数据集，这些数据集通常由人类标记，并在长时间的测量过程中获得。这一假设将通过制作和提取具有各向异性刚度、质量密度和Willis参数张量的弹性超材料的材料参数来验证。这项研究将考虑从近场和远场测量中提取材料属性。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。