Excellence in Research: Experiment Efficient Modeling Method of Dynamic Systems Based on Short-Term Dependency and Non-Recurrent Neural Networks

卓越研究：基于短期依赖和非循环神经网络的动态系统实验高效建模方法

• 批准号: 2100956
• 负责人: Lichun Li
• 金额: $ 33.2万
• 依托单位: Florida Agricultural and Mechanical University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2100956&HistoricalAwards=false
• 关键词: Excellence; Research; Experiment; Efficient; Modeling

This grant will support research to contribute new knowledge related to dynamic systems, promoting a modeling method that is experiment efficient and accelerating the research and development processes. Current modeling methods either require comprehensive understanding, which is difficult for complex systems, or extensive experiment effort, which is impractical due to the expense and time. However, a limited number of experiments does not always mean a limited amount of data. With advanced sensing techniques, abundant in-situ data can be collected in every experiment. This award supports fundamental research to provide needed knowledge to extract abundant independent data from the in-situ data in a limited number of experiments. The new knowledge will provide enough data to train neural network models even with a limited number of experiments and help reduce the time and cost to model dynamic systems. As dynamic systems are common in aerospace, manufacturing, material science, and civil systems, the results from this research will benefit the U.S. economy and society. This grant will support women students and broaden the participation of women in science, technology, engineering, and math. A micro-scale supporting network for women engineering students will be built within the PI’s group, which will connect the PI, graduate students, undergraduate students, and K-6 children on a regular basis to encourage women to pursue an engineering career. Practitioners find that short-term memory feed-forward neural networks and infinite memory recursive neural networks have comparable performance in some dynamic systems. This project is to study this phenomenon based on the well-known observability property of dynamic systems, and will focus on two specific case studies, fiber orientation in additive manufacturing and nanotube network quality of continuous nanotube thin film. Different from the existing observability criteria that rely on the full system knowledge, the observability criteria built in this project only depend on the in-situ data and/or the partial system knowledge. Based on the short-term dependency study, abundant independent data will be extracted from a limited number of experiments to train feed-forward neural network models. Based on the partial knowledge of the dynamic systems, this project is for a customized feed-forward neural network structure to achieve further data efficiency.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.

该补助金将支持研究，以贡献与动态系统相关的新知识，促进实验效率高的建模方法，并加速研究和开发过程。 目前的建模方法要么需要全面的理解，这是很难的复杂系统，或广泛的实验工作，这是不切实际的，由于费用和时间。然而，有限数量的实验并不总是意味着有限数量的数据。利用先进的传感技术，可以在每次实验中收集到丰富的现场数据。该奖项支持基础研究，以提供所需的知识，在有限数量的实验中从原位数据中提取丰富的独立数据。新知识将提供足够的数据来训练神经网络模型，即使实验数量有限，也有助于减少建模动态系统的时间和成本。由于动态系统在航空航天、制造业、材料科学和民用系统中很常见，因此这项研究的结果将有利于美国的经济和社会。该补助金将支持女学生，扩大妇女在科学、技术、工程和数学领域的参与，并将在PI小组内建立一个微型的女工程学生支持网络，该网络将定期连接PI、研究生、本科生和K-6儿童，以鼓励妇女从事工程职业。 实践者发现，短期记忆前馈神经网络和无限记忆递归神经网络在某些动态系统中具有相当的性能。本项目基于动力学系统的可观测性来研究这一现象，并将重点研究两个具体案例，增材制造中的纤维取向和连续纳米管薄膜的纳米管网络质量。与现有的依赖于完整系统知识的可观测性准则不同，本项目建立的可观测性准则仅依赖于现场数据和/或部分系统知识。在短期相关性研究的基础上，从有限数量的实验中提取丰富的独立数据来训练前馈神经网络模型。基于对动态系统的部分了解，该项目是一个定制的前馈神经网络结构，以实现进一步的数据效率。该奖项反映了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。

项目成果

Analyzing the Short-Term Dependency in Ultra-High Magnetic Response Systems - Modeling Sequential Data with Non-Recurrent Neural Networks

分析超高磁响应系统的短期依赖性 - 使用非循环神经网络建模序列数据

• DOI: 10.1016/j.procs.2021.05.044
• 发表时间: 2021
• 期刊: Procedia Computer Science
• 作者: Sun, Jieming;Li, Lichun
• 通讯作者: Li, Lichun

Short-term dependency of a class of nonlinear continuous time dynamic systems

一类非线性连续时间动态系统的短期依赖性

• DOI: 10.1016/j.engappai.2021.104402
• 发表时间: 2021
• 期刊: Engineering Applications of Artificial Intelligence
• 影响因子: 8
• 作者: Sun, Jieming;Li, Lichun
• 通讯作者: Li, Lichun