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CAREER: Advancing Efficient Global Optimization of Extremely Expensive Functions under Uncertainty using Structure-Exploiting Bayesian Methods

职业：使用结构利用贝叶斯方法在不确定性下推进极其昂贵的函数的高效全局优化

基本信息

批准号：
2237616
负责人：
Joel Paulson
金额：
$ 51.75万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-02-01 至 2028-01-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237616&HistoricalAwards=false
关键词：
CAREER Advancing Efficient Global Optimization

项目摘要

Mathematical optimization is the process of maximizing a performance or quality indicator by identifying the best possible value among the set of all feasible options. Optimization problems arise in virtually all human endeavors related to decision making including engineering, economics, sustainability, healthcare, and manufacturing. Instances of such optimization problems are particularly challenging to solve whenever evaluating performance and/or testing for feasibility requires an expensive simulation or experiment whose results may be corrupted by random errors. Bayesian optimization (BO) is a class of machine learning-based optimization algorithms that has recently been shown to achieve state-of-the-art performance in several important applications from this problem class such as in deep machine learning, validation of expensive simulators, and material and drug design. However, traditional BO methods treat the mathematical functions that model performance and feasibility as black boxes with unknown structure, which sets a fundamental limit on their computational efficiency. This observation is the key motivation for this research project, which looks to overcome these efficiency barriers via the development of new algorithms that exploit known problem structures within the Bayesian framework. These novel capabilities will be applied to three unsolved problems currently impacting society: (1) identifying unknown mechanisms in cellular decision-making processes for biomanufacturing; (2) discovery of new sustainable and economical lithium-ion battery electrode materials; and (3) real-time energy minimization in industrial heating, ventilation, and air conditioning (HVAC) systems. In addition, the project looks to tightly integrate research and educational activities through the development of interactive workshops and games related to decision science, which will be made accessible to the public. Through collaboration with local educators, planned outreach activities also will provide K-12 students from underrepresented communities with opportunities to learn about decision science.The proposed optimization methodology is inspired by the principle of grey-box modeling, which states that one should avoid learning what is already known when applying machine learning methods. The investigator conjectures a significant reduction in experimental and/or computational effort can be obtained in practice over traditional Bayesian optimization (BO) methods by properly leveraging prior (or domain) knowledge, which is almost always available in practice. Since prior knowledge can come in many diverse forms, the proposed research will focus on some of the most common and important examples. The three specific research aims are: (1) optimizing with hybrid physics-based and data-driven models given noisy and incomplete datasets; (2) optimizing with constrained multi-fidelity models that fuse data from a collection of heterogeneous sources of variable accuracy and cost; and (3) scaling to high-dimensional and sparse data problems through the incorporation of non-myopic and graph-structured formulations. The proposed research aims to promote convergence of statistics, machine learning, optimization, and process systems engineering. More broadly, the improved methods developed as a part of this research project will allow practitioners to solve a wide range of grey-box optimization problems with greater speed and accuracy. Planned outreach activities include educating K-12 students about decision-making under uncertainty via interactive workshops and games, incorporating new data-driven optimization material into the chemical engineering curriculum, and organizing cross-disciplinary professional workshops on the potential significance and impacts of cutting-edge BO technology.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.

数学优化是通过在所有可行的选项集合中确定最佳的可能值来最大化绩效或质量指标的过程。优化问题出现在几乎所有与决策相关的人类工作中，包括工程、经济、可持续发展、医疗保健和制造。每当评估性能和/或测试可行性需要昂贵的模拟或实验，其结果可能被随机误差破坏时，此类优化问题的实例尤其难以解决。贝叶斯优化(BO)是一类基于机器学习的优化算法，最近被证明在这类问题的几个重要应用中取得了最先进的性能，例如在深度机器学习、昂贵模拟器的验证以及材料和药物设计中。然而，传统的BO方法将模拟性能和可行性的数学函数视为结构未知的黑盒，这对其计算效率设置了基本限制。这一观察结果是本研究项目的关键动机，该项目寻求通过开发利用贝叶斯框架内已知问题结构的新算法来克服这些效率障碍。这些新能力将应用于目前影响社会的三个尚未解决的问题：(1)识别生物制造的细胞决策过程中的未知机制；(2)发现新的可持续和经济的锂离子电池电极材料；(3)工业供暖、通风和空调(HVAC)系统中的实时能量最小化。此外，该项目希望通过开发与决策科学有关的互动讲习班和游戏，将研究和教育活动紧密结合起来，向公众开放。通过与当地教育工作者的合作，计划中的外展活动还将为来自代表性不足社区的K-12学生提供学习决策科学的机会。拟议的优化方法受到灰箱建模原理的启发，即在应用机器学习方法时应避免学习已知的知识。研究人员推测，与传统的贝叶斯优化(BO)方法相比，通过适当利用实践中几乎总是可用的先验(或领域)知识，可以在实践中显著减少实验和/或计算工作量。由于先验知识可以有许多不同的形式，拟议的研究将集中在一些最常见和最重要的例子上。三个具体的研究目标是：(1)使用混合物理模型和数据驱动模型优化给定的噪声和不完整数据集；(2)使用受约束的多保真模型进行优化，该模型融合了不同精度和成本的异质来源的数据；以及(3)通过结合非短视和图形结构的公式来扩展到高维和稀疏数据问题。拟议的研究旨在促进统计学、机器学习、优化和过程系统工程的融合。更广泛地说，作为本研究项目的一部分开发的改进方法将允许实践者以更快的速度和更准确的速度解决广泛的灰箱优化问题。计划的外展活动包括通过互动工作坊和游戏教育K-12学生如何在不确定的情况下做出决策，将新的数据驱动的优化材料纳入化学工程课程，以及组织关于尖端BO技术的潜在意义和影响的跨学科专业研讨会。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。