CAREER: Uncertainty Quantification and Optimization with Hybrid Models for Molecular-to-Systems Engineering

多尺度建模将分子、材料、设备、系统和基础设施尺度结合到整体方法中，以创建满足国家和全球需求的目标技术。然而，大多数多尺度框架需要过度简化以确保合理的计算时间，即使在使用超级计算机时也是如此。这些过度简化会带来不确定性，并可能影响分析和决策。研究者寻求建立新的混合建模方法，以明确量化，传播和减轻不确定性（即信息损失）跨越巨大的长度和时间尺度。研究者预测，混合模型将通过更精确和可预测的多尺度模型提高化学制造和能量转换系统的安全性、增强性能、减少环境影响并提高效率。混合模型支持的多尺度工程框架可以加速许多具有国家重要性的科学、工程和公共政策领域的发展，包括气候变化、水资源短缺和先进制造业，通过在科学家、工程师和决策者的跨学科团队之间实现更有效的自上而下和自下而上的整合。该项目利用交互式模块和云计算，将所有年级的统计和计算结合起来，将教育和研究结合起来。在与当地教育工作者的合作下，研究者计划创建与6 - 12年级计算机科学课程相一致的教育模块。还将开发将统计和计算纳入化学工程本科和研究生课程的教育模块。在可能的情况下，研究软件和教育材料将在网上免费分发，以最大限度地发挥影响。该CAREER计划旨在建立严格的数学框架，以量化多尺度模型缩减带来的信息损失和诱导的认知不确定性。混合模型可以通过使用数据驱动的机器学习结构（例如，高斯过程）来增加基于物理的方程来量化缺失、未知或简化物理的影响，从而克服这一挑战。研究者将解决三个基本研究问题，以实现分子到系统工程的可扩展混合建模：(1)如何有效地将混合模型嵌入优化问题中，从而扩展不确定性范式下的优化，以考虑认知（即模型形式）的不确定性；(2)如何利用近似变分推理技术将混合模型的训练速度提高几个数量级？(3)如何计算混合动力模型的试验优化设计（DOE） ？这种方法将促进统计学、机器学习、计算优化和化学工程的融合。将研究稀疏网格和压缩感知，以实现不确定性下的可处理优化，包括认知不确定性下反应工程的新框架。同样，10倍到100倍快的混合模型训练可以实现在线控制。推广基于模型的DOE和贝叶斯优化形式可以使DOE适用于混合模型，为协作团队中最大化资源约束实验提供新功能。云托管的Jupyter笔记本被提议整合从研究生到中学的所有课程的计算和统计，帮助确保未来的美国劳动力在未来几十年里充分利用混合模型和机器学习的进步。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。

Multiscale modeling combines molecular, material, device, system, and infrastructure scales into holistic approaches to create targeted technologies that meet national and global needs. However, most multiscale frameworks require over-simplification to ensure reasonable computation time, even when using a supercomputer. These over-simplifications introduce uncertainty and can bias analyses and decisions. The investigator seeks to establish new hybrid modeling methodologies to explicitly quantify, propagate, and mitigate uncertainty (i.e., information loss) across vast length and timescales. The investigator predicts that hybrid models will improve safety, enhance performance, reduce environmental impact, and improve efficiency in chemical manufacturing and energy conversion systems through more accurate and predictive multiscale models. Multiscale engineering frameworks enabled by hybrid models can accelerate many domains of science, engineering, and public policy of national importance, including climate change, water scarcity, and advanced manufacturing by enabling more effective top-down and bottom-up integration across interdisciplinary teams of scientists, engineers, and decision-makers. The project enmeshes education and research by leveraging interactive modules and cloud-computing to integrate statistics and computing at all grade levels. In partnership with local educators, the investigator plans to create educational modules aligned with computer science curricula for grades 6 - 12. Educational modules to incorporate statistics and computing across the chemical engineering undergraduate and graduate curricula will also be developed. Whenever possible, research software and educational materials will be distributed online for free to maximize impact.This CAREER program seeks to establish rigorous mathematical frameworks to quantify the information loss and induced epistemic uncertainty from multiscale model reduction. Hybrid models can overcome this challenge by augmenting physics-based equations with data-driven machine learning constructs (e.g., Gaussian Processes) to quantify effects of missing, unknown, or simplified physics. The investigator will address three fundamental research questions to enable scalable hybrid modeling for molecular-to-systems engineering: (1) How to efficiently embed hybrid models in optimization problems, thereby extending optimization under uncertainty paradigms to consider epistemic (i.e., model-form) uncertainty?; (2) How to leverage approximate variational inference techniques to accelerate hybrid model training by orders of magnitude?; and (3) How to compute optimal design of experiments (DOE) for hybrid models? This approach will promote convergence of statistics, machine learning, computational optimization, and chemical engineering. Sparse grids and compressed sensing will be examined to enable tractable optimization under uncertainty including new frameworks for reaction engineering under epistemic uncertainty. Likewise, 10x to 100x faster hybrid model training could enable online control. Generalizing model-based DOE and Bayesian optimization formalism could enable DOE for hybrid models, offering new capabilities to maximize resource-constrained experiments in collaborative teams. Cloud-hosted Jupyter notebooks are proposed to integrate computing and statistics across curricula from graduate to middle school levels, helping to ensure the future U.S. workforce is well-equipped to leverage hybrid models and machine learning advances for decades to come.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.