MCA: Physics-Informed Machine Learning from Acoustic Data

MCA：基于声学数据的物理机器学习

• 批准号: 2121005
• 负责人: Parisa Shokouhi
• 金额: $ 36.23万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2121005&HistoricalAwards=false
• 关键词: MCA; Physics; Informed; Machine; Learning

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.

这项中期职业发展（MCA）补助金将资助研究和培训，使声学传感器能够有效地使用，以估计材料特性，诊断状态，或预测工程设备和结构的故障，以及对矿山，碳封存地点和地热能水库的地震监测，从而促进科学的进步，促进国家的繁荣和福利。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，来自这种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。这种数据利用不足导致特征分辨率差，无法准确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，该框架考虑了完整的声学信号波形，并通过基础物理知识来了解，从而提高其预测的准确性，可推广性和可解释性。该框架提供了一种非传统的方法，在航空航天，汽车，基础设施，管道和能源行业的无损缺陷检测，以及长期面临的挑战，地震预测，并可能转化为改善分类异常医学超声成像。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水平课程，将声学和机器学习联系起来，并提供在线课程材料。本研究旨在为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的整合做出基础性贡献，该数据分析框架可以提取信息，从声学数据中获得丰富的关键特征集。它通过构建基于物理学的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。这种模型预计将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，并更易于解释，允许对波在复杂介质中的传播和相互作用有新的理解，例如，不均匀性和不连续性。首先，训练结合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信息量最大的声学特征。接下来，模型的构建，也尊重耦合摩擦和3D波传输定律。最后，模型的可解释性，鲁棒性和概括性进行评估，对一个更大的实验条件范围内和范围外的训练data.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。