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

这项研究将获得有关无损评估所有机械参数的新知识，这些参数充分描述了复杂介质（如生物组织和具有精心设计的微观结构的设计材料）的弹性行为。以前的非破坏性评估方法通常依赖于对探测材料性质的限制性假设（例如，方向无关性），需要大样本进行评估，和/或只提供材料参数的一小部分。例如，准确地评估生物组织的所有力学参数是很重要的，因为它告知由于病理过程引起的组织变化。然而，用于探测这些参数的最先进的方法（例如弹性成像）可能只提供一小部分机械参数，这可能会延迟组织病理的检测。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从基础设施完整性评估到非侵入性医疗诊断等众多领域所利用，从而在多个层面上造福社会。这项研究还将为开发远程访问和控制的在线波动动力学实验室提供机会，这将增加经济困难学生和代表性不足的少数民族对尖端实验科学的参与。目前用于从散射机械波中提取物质动态力学特性的分析方法被证明在几十个参数未知的情况下是不够的。这项工作将获得新的知识和工具，以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。核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，这与大多数需要昂贵的训练数据集的机器学习应用形成鲜明对比，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。这一假设将通过制造和提取具有各向异性刚度、质量密度和威利斯参数张量的弹性超材料的材料参数来验证。本研究将考虑从近场和远场测量中提取材料性质。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.