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、系统和基础设施的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。这些过于简单化带来了不确定性，并可能使分析和决策产生偏差。研究人员试图建立新的混合建模方法，以明确地量化、传播和减轻跨越巨大长度和时间尺度的不确定性(即，信息损失)。研究人员预测，混合模型将通过更准确和更具预测性的多尺度模型，在化学制造和能源转换系统中提高安全性、增强性能、减少环境影响和提高效率。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，包括气候变化、水资源短缺和先进制造业，方法是在科学家、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集成。该项目利用互动模块和云计算来整合所有年级的统计和计算，从而将教育和研究结合在一起。该研究人员计划与当地教育工作者合作，为6-12年级创建与计算机科学课程相一致的教育模块。还将开发将统计学和计算纳入整个化学工程本科和研究生课程的教育模块。只要有可能，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。该职业计划寻求建立严格的数学框架，以量化多尺度模型简化所造成的信息损失和引发的认知不确定性。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)来增强基于物理的方程来量化缺失、未知或简化的物理的影响，从而克服这一挑战。研究人员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，从而将不确定范式下的优化扩展到考虑认知(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加速数量级？以及(3)如何计算混合模型的最优实验设计(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传感，以实现在不确定情况下的易于处理的优化，包括在认知不确定情况下的反应工程的新框架。同样，混合模型训练的速度提高了10倍到100倍，可以实现在线控制。推广基于模型的DOE和贝叶斯优化形式，可以实现混合模型的DOE，为协作团队中最大化资源受限的实验提供新的能力。云托管的Jupyter笔记本旨在整合从研究生到中学的课程中的计算和统计，帮助确保未来的美国劳动力在未来几十年内充分利用混合模型和机器学习的进步。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。

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.