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

合作研究：利用有限的成像数据推断嵌入式光纤网络的原位微观力学

基本信息

批准号：
2127864
负责人：
Emma Lejeune
金额：
$ 28.69万
依托单位：
Trustees of Boston University
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：
2022-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2127864&HistoricalAwards=false
关键词：
Collaborative Research Inferring Situ Micro

项目摘要

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.

该赠款将着重于获得对嵌入式纤维网络的基本了解，并创建从有限的可用测量中表征其行为所需的工具。嵌入的纤维网络本质上是普遍存在的，从周围的生物细胞周围的细胞外基质到嵌入器官中的血管到蛾的茧。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以生物启发的高级材料的基本机械构建块。重要的是要了解这些系统，而且要以非破坏性方式衡量其机械行为，以便可以在现实世界中应用理解的进步。该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，以阐明嵌入矩阵属性，纤维特性和纤维网络性能之间的基本关系，用于软嵌入的光纤网络，并具有大变形。此外，该研究项目将开发计算能力，以分析这些系统，在这些系统中，基于图像的严格数据被用于预测结构属性和表征机械行为。该研究将通过在开源许可下传播相关数据和代码来完成，并释放着针对机械学习将机器学习应用于机械研究的在线模块。这项研究还将通过在中学和高中建立教育外展计划来完成，这些课程专注于将STEM教育带到服务不足的人群中。这项研究的具体目标是定义经历大变形的软嵌入式纤维网络中的基本结构 - 功能关系，并创建分析这些系统所需的工具，但如果有限的可用成像数据。至关重要的是，有必要开发工具以非破坏性评估这些系统，因为它们最重要的应用之一是在生命系统中。这是该项目的研究目标包括：（i）策划实验数据集并实施和验证三维嵌入式光纤网络的计算模型，经历了大变形；（ii）理解和描述经历较大变形的嵌入式纤维网络的不同机械状态；（iii）从离散纤维尺度上拍摄的不完美的图像中，建立和测试机器学习框架以快速和非破坏性分析的嵌入式光纤网络。该项目将允许PI在应用机制，计算机制和机器学习的界面上促进知识库，并在材料和结构的机制中建立长期职业。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的评估来审查Criteria通过评估来通过评估来获得的支持。