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

这笔赠款将支持研究，以贡献与动态系统相关的新知识，促进实验高效的建模方法，并加快研究和开发进程。目前的建模方法要么需要全面的理解，这对复杂系统来说是困难的，要么需要大量的实验工作，由于费用和时间的原因，这是不切实际的。然而，有限的实验次数并不总是意味着有限的数据量。有了先进的传感技术，每一次实验都可以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，从有限数量的实验中从现场数据中提取丰富的独立数据。新的知识将提供足够的数据来训练神经网络模型，即使是有限数量的实验，并有助于减少对动态系统建模的时间和成本。由于动力系统在航空航天、制造、材料科学和民用系统中很常见，因此这项研究的结果将使美国经济和社会受益。这笔赠款将支持女性学生，并扩大女性在科学、技术、工程和数学领域的参与。在PI小组内将建立一个微型的工科女学生支持网络，定期将PI、研究生、本科生和K-6儿童联系起来，鼓励女性从事工程事业。实践者发现，在某些动态系统中，短期记忆前馈神经网络和无限记忆递归神经网络具有相当的性能。本项目是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的案例：添加剂制造中的纤维取向和连续纳米管薄膜的纳米管网络质量。与现有的依赖于全系统知识的可观测性判据不同，本项目建立的可观测性判据仅依赖于现场数据和/或部分系统知识。在短期依赖研究的基础上，从有限的实验中提取大量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知识，该项目是为了定制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 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.