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的高效和有效设计，如智能工厂和用于材料处理的智能自主系统，从而为国家繁荣和经济福利做出贡献。设计这类系统的一个主要挑战是，它们的性能可能会随着设计变量的微小变化而突然变化，从而产生不连续的设计响应。这笔赠款将开发有效的代理建模方法，以预测存在这种不连续情况下的设计性能，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，促进具有数据科学能力的劳动力的发展。研究团队将广泛传播他们的研究成果，并向数据科学和系统工程界分享数据和由此产生的软件包。这项研究将在代理建模、顺序设计、主动学习、系统设计和先进制造领域做出实质性贡献。系统性能被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制度变化，基于估计的分区对局部数据进行分割(S)。只有属于与预测位置相同制度的本地数据才会影响模型预测。研究活动将探索两个想法：(1)使用本地数据选择的本地GP建模；(2)使用概率区域估计来增加设计变量的更平滑的替代方案。还将研究一种顺序设计方法，以优化训练新代理模型的数据获取计划。由此产生的新的元模型和顺序设计方案将使用碳纳米管合成和智能材料处理系统中的设计问题进行验证。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 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.