CDS&E/Collaborative Research: Local Gaussian Process Approaches for Predicting Jump Behaviors of Engineering Systems

该奖项将通过开发工具来支持现代工程系统（如智能工厂和智能自动化系统）的高效设计，为国家繁荣和经济福利做出贡献。设计此类系统的一个主要挑战是，它们的性能可能随着设计变量的微小变化而突然改变，从而产生不连续的设计响应。这笔拨款将开发有效的替代建模方法，以预测存在此类不连续的设计性能，然后可以在设计优化中加以利用。这项工作将有助于解决复杂的工程设计问题，并将在碳纳米管智能制造系统的设计和自动化材料处理系统的设计中进行评估。该奖项还将通过为K-12、本科生和研究生提供多学科研究、培训和国际合作机会，促进数据科学能力劳动力的发展。研究小组将广泛传播他们的研究成果，并向数据科学和系统工程社区分享数据和由此产生的软件包。本研究将对代理建模、顺序设计、主动学习、系统设计和先进制造等领域做出重大贡献。系统性能建模为设计变量的分段连续函数，激励局部高斯过程（GP）代理建模。该方法适应预测位置周围的状态变化，根据估计的分区对局部数据进行分割。只有与预测位置属于同一区域的局部数据才会影响模型的预测。研究活动将探索两个思路：(1)局部数据选择的局部GP建模；(2)更平滑的替代方案，增加设计变量与概率制度估计。一个顺序设计的方法来优化数据采集计划训练新的代理模型也将被研究。由此产生的新元模型和顺序设计方案将通过碳纳米管合成和智能材料处理系统的设计问题进行验证。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award will contribute to national prosperity and economic welfare by developing tools to support the efficient and effective design of modern engineering systems such as smart factories and smart autonomous systems for material handling. A major challenge in designing such systems is that their performance can change abruptly with small changes in design variables, creating discontinuous design responses. This grant will develop efficient surrogate modeling methods to predict design performance in the presence of such discontinuities which can then be exploited in design optimization. This work will facilitate the solution of complex engineering design problems and will be evaluated in the design of a smart manufacturing system for carbon nanotubes, and the design of automated material handling systems. The award will also contribute to the development of a data science-capable workforce by providing multidisciplinary research, training, and international collaboration opportunities for K-12, undergraduate, and graduate students. The research team will broadly disseminate their research findings and share data and the resulting software packages to the data science and systems engineering community. This research will make substantial contributions to the areas of surrogate modeling, sequential design, active learning, system design, and advanced manufacturing. System performance is modeled as a piece-wise continuous function of design variables, motivating local Gaussian process (GP) surrogate modeling. The approach accommodates regime changes around a prediction location, segmenting local data based on the estimated partition(s). Only the local data belonging to the same regime as a prediction location affects the model prediction. Research activities will explore two ideas: (1) local GP modeling with local data selection; and (2) smoother alternatives that augment design variables with probabilistic regime estimates. A sequential design approach to optimize data acquisition plans for training the new surrogate models will also be investigated. The resulting new meta-models and sequential design scheme will be validated using design problems in carbon nanotube synthesis and smart material handling systems.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.