MCA: Physics-Informed Machine Learning from Acoustic Data

这笔职业生涯中期促进(MCA)赠款将用于研究和培训，使之能够有效地使用声传感器来估计材料特性、诊断状态或预测工程设备和结构中的故障，以及用于矿山、碳汇地点和地热能水库的地震监测，从而促进科学进步和促进国家繁荣和福祉。声学传感器的范围从用于医疗诊断和结构健康监测的微型换能器到用于记录大规模地面运动的地震仪。在目前的实践中，来自这类传感器的时变信号从数千个数据点减少到几个手工制作的特征。这种对数据的不充分利用导致了较差的特征分辨率，并且无法准确地诊断材料的演变状态或预测即将发生的故障。该项目将通过建立一个分析和建模框架来克服这些缺点，该框架可以解释完整的声音信号波形，并由基本物理知识提供信息，从而提高其预测的准确性、概括性和可解释性。这一框架为航空航天、汽车、基础设施、管道和能源行业的无损缺陷检测以及地震预测的长期挑战提供了一种非常规方法，并可能转化为对医学超声成像中异常的改进分类。培训计划的一个组成部分是开发一个新的基于项目的研究生级课程，连接声学和机器学习，并在线提供课程材料。这项研究旨在为将机器学习技术与复杂材料系统弹性动力学响应的特定领域知识在数据分析框架中的集成做出基本贡献，该数据分析框架从声学数据中提取一组信息丰富的关键特征。它通过构建物理信息深度学习模型来实现这一目标，该模型使用岩石实验室实验的超声波数据来预测剪切破坏。这样的模型有望最大限度地减少过度拟合的风险，从而使它们也适用于其他数据集，并且更具解释性，允许对波的传播和相互作用有新的理解，例如，在具有非均质性和不连续性的复杂介质中。首先，训练融合了降维技术和多任务学习的多头模型，以在有限的数据集中识别最具信息量的声学特征。其次，建立了同时考虑耦合摩擦和三维波传播规律的模型。最后，模型的可解释性、稳健性和概括性将根据一组更大的实验条件在培训数据的范围内和外部进行评估。该奖项反映了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。

This Mid-Career Advancement (MCA) grant will fund research and training that enables effective use of acoustic sensors to estimate material properties, diagnose state, or predict failure in engineered devices and structures, as well as for seismic monitoring of mines, carbon sequestration sites, and geothermal energy reservoirs, thereby promoting the progress of science and advancing the national prosperity and welfare. Acoustic sensors range from miniature transducers for medical diagnostics and structural health monitoring to seismometers for recording large-scale ground motion. In current practice, the time-varying signals from such sensors are reduced from thousands of data points to a few hand-crafted features. This underutilization of data results in poor feature resolution and an inability to accurately diagnose the evolving state of materials or predict imminent failures. This project will overcome such shortcomings by building an analysis and modeling framework that accounts for the full acoustic signal waveform and is informed by knowledge of the underlying physics, thereby achieving improved accuracy, generalizability, and interpretability of its predictions. This framework offers an unconventional approach to nondestructive defect detection in aerospace, automotive, infrastructure, pipelines, and energy industries, as well as to the longstanding challenge of earthquake prediction, and may translate into improved classification of abnormalities in medical ultrasound imaging. An integral component of the training plan is the development of a new project-based graduate-level course bridging acoustics and machine learning and with course materials made available online.This research aims to make fundamental contributions to the integration of machine-learning techniques with domain-specific knowledge about the elastodynamic response of complex materials systems in a data analysis framework that extracts an information-rich set of critical features from acoustic data. It achieves this goal by constructing physics-informed deep learning models to predict shear failure using ultrasonic data from laboratory experiments on rocks. Such models are expected to minimize the risk of overfitting, thereby making them adaptable also to other datasets, and to be more explainable, permitting new understanding of wave propagation and interactions in complex media, for example, with heterogeneities and discontinuities. First, multi-headed models that incorporate dimensional reduction techniques and multi-task learning are trained to identify the most informative acoustic features in a limited dataset. Next, models are constructed that also respect coupled friction and 3D wave transmission laws. Finally, model interpretability, robustness, and generalizability are evaluated against a larger set of experimental conditions within and outside the bounds of the training data.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.