CAREER: Design Under Uncertainty in Combinatorially Expanding Spaces

职业：组合扩展空间的不确定性下的设计

• 批准号: 2238038
• 负责人: Ramin Bostanabad
• 金额: $ 57.42万
• 依托单位: University of California-Irvine
• 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: 2023-04-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238038&HistoricalAwards=false
• 关键词: CAREER; Design; Under; Uncertainty; Combinatorially

The goal of this Faculty Early Career Development (CAREER) award is to develop a framework to optimally design a material’s composition at the small length scales and, in turn, quantify its effects on the component properties. Composition optimization of engineered materials is essential to many applications that are crucial to our national health, prosperity, and welfare. This type of design optimization, however, has two major challenges that dramatically reduce the efficiency of designers: (1) co-existence of multiple uncertainty sources (e.g., lack of data, inaccurate simulations, unknown parameters), and (2) existence of a vast disjoint design space that has both categorical and quantitative features. To address these issues and accelerate the design process, this project will convert the design problem into a statistical learning one. This conversion uniquely leverages machine learning and enables designers to make informed decisions for resource allocation, uncertainty quantification, and anomaly/novelty detection. The impact of the framework will be demonstrated on optimal design of complex concentrated alloys, which have shown great potential in critical applications involving energy storage, cryogenic operating conditions, and more. The research outcomes of this project will be tightly integrated with multiple educational and outreach activities to benefit educators and students (at both high school and university levels), as well as businesses. These activities will produce educational content (including codes and video tutorials) and a user-friendly app that small businesses and high schoolers can leverage for design optimization under uncertainty. These activities will collectively demonstrate to students and practitioners that engineering design and machine learning can dramatically increase our capabilities in solving complex engineering problems.The hypothesis behind this research is that with appropriate conversion operators and learning mechanisms, the combinatorial design space and associated uncertainties can be encoded via a set of low-dimensional and interpretable manifolds, each of which is a compact representation of high-dimensional objects (e.g., an uncertainty source). This hypothesis will be tested in four research thrusts to make the following contributions: (1) Developing an uncertainty representation method that improves uncertainty quantification capabilities and also provides visually interpretable diagnostic measures for detecting model form errors and existence of non-Gaussian uncertainties; (2) Establishing a design representation methodology for encoding a vast combinatorial design space in a compact manifold that designers can leverage to identify promising combinations; (3) Introducing new optimality metrics for probabilistic metamodeling and resource allocation; (4) Developing a multi-fidelity multiscale modeling framework that enables homogenization-based multiscale simulations to dynamically and automatically adjust the fidelity (and hence, cost) and calibration parameters of the nested simulations. These methodological contributions are generic and can benefit a broad range of applications, such as multi-disciplinary systems analysis. The education and outreach components of this project include developing a design optimization and calibration app that can be used by high school students, educators, and local small businesses; transferring the technology on materials optimization to the manufacturing industry; and developing educational materials and summer workshop series.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.

这个教师早期职业发展（CAREER）奖的目标是开发一个框架，以最佳设计材料的组成在小的长度尺度，反过来，量化其对组件性能的影响。工程材料的成分优化对于许多对我们国家的健康、繁荣和福利至关重要的应用至关重要。然而，这种类型的设计优化具有两个主要挑战，其显著降低了设计者的效率：（1）多个不确定性源（例如，缺乏数据、不准确的模拟、未知的参数），以及（2）存在具有分类和定量特征的巨大的不相交设计空间。为了解决这些问题并加速设计过程，该项目将设计问题转化为统计学习问题。这种转换独特地利用了机器学习，使设计人员能够为资源分配、不确定性量化和异常/新奇检测做出明智的决策。该框架的影响将在复杂浓缩合金的优化设计上得到证明，这些合金在涉及能量存储、低温操作条件等的关键应用中显示出巨大的潜力。该项目的研究成果将与多种教育和推广活动紧密结合，使教育工作者和学生（高中和大学）以及企业受益。这些活动将产生教育内容（包括代码和视频教程）和用户友好的应用程序，小企业和高中生可以利用这些内容在不确定性下进行设计优化。这些活动将共同向学生和从业者证明，工程设计和机器学习可以大大提高我们解决复杂工程问题的能力。这项研究背后的假设是，通过适当的转换算子和学习机制，组合设计空间和相关的不确定性可以通过一组低维和可解释的流形进行编码，其中每一个都是高维对象的紧凑表示（例如，不确定性来源）。本文将从四个方面对这一假设进行检验，以期做出以下贡献：（1）开发一种不确定性表示方法，提高不确定性量化能力，并提供视觉上可解释的诊断措施，用于检测模型形式误差和非高斯不确定性的存在;（二）建立一种设计表示方法，用于在一个紧凑的流形中编码一个巨大的组合设计空间，设计人员可以利用该空间来识别有前途的设计。组合;（3）为概率元建模和资源分配引入新的最优性度量;（4）开发多保真度多尺度建模框架，使基于均匀化的多尺度模拟能够动态和自动地调整嵌套模拟的保真度（以及成本）和校准参数。这些方法的贡献是通用的，可以受益于广泛的应用，如多学科系统分析。该项目的教育和推广部分包括开发一个设计优化和校准应用程序，可供高中生，教育工作者和当地小企业使用;将材料优化技术转移到制造业;该奖项反映了NSF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。