Collaborative Research: Design, Modeling and Active Learning of Quantitative-Sequence Experiments

协作研究：定量序列实验的设计、建模和主动学习

• 批准号: 2311187
• 负责人: Xinwei Deng
• 金额: $ 18.2万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311187&HistoricalAwards=false
• 关键词: Collaborative; Research; Design; Modeling; Active

A new type of experiment concerning both quantitative and sequence (QS) factors has recently drawn great attention in science and engineering applications. In chemotherapy, to develop efficient drug combinations involving several drug components, researchers need to conduct experiments optimizing both the doses and the sequence orders of drug components. Such a problem raises new challenges for statisticians since the input space is semi-discrete and grows exponentially with the number of drugs. Researchers rely more than ever on statistical modeling and active learning to identify optimal settings given limited experimental resources. Additionally, QS experiments often have specific requirements. In the computer experiment for metal additive manufacturing processes, the output response is binary (success/failure), and it requires both interpolation and uncertainty quantification, which is an unsolved problem in the current literature. In this project, the investigators will provide systematic solutions to QS experiments, addressing challenges in design, modeling, uncertainty quantification, and active learning. The outcome of this project will help save experimental costs in applications involving QS factors. The applications to chemotherapy will help advance cancer research in the U.S., while the applications to manufacturing processes will enhance the industrial competitiveness of the U.S. Also, this project provides research training opportunities for graduate students. Active learning in experiments, aka reinforcement learning under the broad context of machine learning, allocates runs in an adaptive manner, which is generally more efficient than one-shot experiments for optimizing the experimental settings. This project will establish new Gaussian process-based models for physical experiments with QS factors, based on which new active learning procedures will be developed. For analyzing computer experiments, a novel Hopfield process (HP) framework will be established as an accurate surrogate for interpolating binary (and categorical) outputs, which will facilitate uncertainty quantification and active learning. Optimal QS experimental designs will also be constructed by combing several Williams-transformed good lattice point sets, which possess desirable properties including space-filling, orthogonality, and paired balance. This research project will provide systematic solutions for various types of QS experiments that are of interest in scientific research and industrial applications.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.

关于定量和序列（QS）因素的一种新型实验最近引起了科学和工程应用的极大关注。在化学疗法中，为了开发涉及多种药物成分的有效药物组合，研究人员需要进行优化药物成分剂量和序列顺序的实验。这样的问题引起了统计学家的新挑战，因为输入空间是半分化的，并且随着毒品的数量而呈指数增长。在有限的实验资源下，研究人员比以往任何时候都更依赖统计建模和积极学习来确定最佳设置。此外，QS实验通常具有特定的要求。在用于金属添加剂制造过程的计算机实验中，输出响应是二进制的（成功/失败），并且需要插值和不确定性量化，这在当前文献中是未解决的问题。在该项目中，研究人员将为QS实验提供系统的解决方案，解决设计，建模，不确定性量化和主动学习方面的挑战。该项目的结果将有助于节省涉及QS因素的应用中的实验成本。化学疗法的应用将有助于推进美国的癌症研究，而制造过程的应用也将增强美国的工业竞争力，该项目为研究生提供了研究培训机会。在实验中的积极学习，又称强化学习在机器学习的广泛背景下，以自适应方式进行分配，这通常比一击实验更有效，以优化实验设置。该项目将基于QS因素建立新的基于高斯的过程模型，以开发新的主动学习程序。为了分析计算机实验，将建立一个新型的Hopfield过程（HP）框架，作为插值二进制（和分类）输出的准确替代物，这将促进不确定性定量和主动学习。最佳QS实验设计也将通过梳理几个威廉姆斯转换的良好晶格集来构建，这些晶格集具有所需的特性，包括空间填充，正交性和配对平衡。该研究项目将为在科学研究和工业应用中感兴趣的各种QS实验提供系统解决方案。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来提供支持。