Collaborative Research: Inferring The In Situ Micro-Mechanics of Embedded Fiber Networks by Leveraging Limited Imaging Data

这笔赠款将专注于获得对嵌入式光纤网络的基本了解，并创建必要的工具，以根据有限的可用测量来表征它们的行为。嵌入的纤维网络在自然界中无处不在，从围绕生物细胞的细胞外基质，到嵌入器官的分支血管，再到蛾子的茧。了解这些系统很重要，因为这些系统是许多类型的天然和工程生物组织以及受生物启发的先进材料的基本机械构件。重要的是不仅要了解这些系统，而且要能够以非破坏性的方式测量它们的机械行为，以便将理解方面的进步应用于现实世界。本研究项目将综合实验、基于理论的计算模型和数据驱动的计算模型来阐明大变形软嵌入光纤网络的嵌入矩阵特性、光纤特性和光纤网络特性之间的基本关系。此外，这项研究项目将开发用于分析这些系统的计算能力，在这些系统中，基于图像的数据严重有限，用于预测结构特性和表征力学行为。这项研究将通过在开放源码许可下传播相关数据和代码，并发布侧重于将机器学习应用于力学研究的在线模块来补充。这项研究还将通过在初中和高中层面建立教育推广计划来补充，这些计划的重点是将STEM教育带给服务不足的人群。这项研究的具体目标是定义经历大变形的软嵌入光纤网络中的基本结构-功能关系，并在有限的可用成像数据的情况下创建分析这些系统所需的工具。至关重要的是，有必要开发工具来非破坏性地评估这些系统，因为它们最重要的应用之一是在生命系统中。因此，本项目的研究目标包括：(I)建立实验数据集，实施和验证三维嵌入式光纤网络大变形的计算模型；(Ii)了解和描绘嵌入式光纤网络大变形的不同力学状态；(Iii)建立和测试机器学习框架，以从离散光纤尺度上拍摄的不完美配对图像快速、无损地分析嵌入式光纤网络。该项目将允许PI在应用力学、计算力学和机器学习的界面上推进知识库，并建立他们在材料和结构力学方面的长期职业生涯。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 grant will focus on gaining a fundamental understanding of embedded fiber networks and creating the tools necessary to characterize their behavior from limited available measurements. Embedded fiber networks are ubiquitous in nature, from the extracellular matrix surrounding biological cells, to branching blood vessels embedded in organs, to moth’s cocoons. Understanding these systems is important because these systems are the fundamental mechanical building blocks of many types of natural and engineered biological tissue, and bio-inspired advanced materials. It is important not only to understand these systems, but also to be able to measure their mechanical behavior in a non-destructive manner so that advances in understanding can be applied in the real world. This research project will synthesize experiments, theory-based computational models, and data-driven computational models to elucidate the fundamental relationship between embedding matrix properties, fiber properties, and fiber network properties for soft embedded fiber networks undergoing large deformation. In addition, this research project will develop computational capabilities for the analysis of these systems where severely limited image-based data is used to predict both structural properties and characterize mechanical behavior. The research will be complemented by disseminating relevant data and code under open source licenses, and releasing online modules focused on applying machine learning to mechanics research. The research will also be complemented by establishing educational outreach programs at the middle school and high school levels that focus on bringing STEM education to underserved populations. The specific goal of this research is to define fundamental structure-function relationships in soft embedded fiber networks undergoing large deformation and create the tools needed to analyze these systems given limited available imaging data. Critically, it is necessary to develop tools to evaluate these systems non-destructively because one of their most important applications is in living systems. Thus, the research objectives of this project include: (i) curating an experimental dataset and implementing and validating a computational model of three-dimensional embedded fiber networks undergoing large deformation; (ii) understanding and delineating the different mechanical regimes of embedded fiber networks undergoing large deformation; (iii) establishing and testing a machine learning framework to rapidly and non-destructively analyze embedded fiber networks from imperfectly-paired images taken on the discrete fiber scale. The project will allow the PIs to advance the knowledge base at the interface of applied mechanics, computational mechanics, and machine learning, and establish their long-term careers in the mechanics of materials and structures.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.