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（职业）奖的目标是开发一个框架，以在小长度尺度上最佳设计材料的构图，然后量化其对组件属性的影响。工程材料的组成优化对于许多对我们的国家健康，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面临两个主要挑战，这些挑战大大降低了设计人员的效率：（1）多个不确定性来源的共存（例如，缺乏数据，不准确的模拟，未知参数），（2）存在具有分类和定量功能的巨大脱节设计空间。为了解决这些问题并加速设计过程，该项目将将设计问题转换为统计学习问题。这种转换独特地利用机器学习并使设计师能够为资源分配，不确定性定量和异常/新颖性检测做出明智的决定。该框架的影响将证明对复杂浓缩合金的最佳设计，这些合金在涉及储能，低温工作条件等的关键应用中显示出很大的潜力。该项目的研究成果将与多项教育和外展活动紧密整合，以使教育者和学生（在高中和大学级别上）以及企业以及企业。这些活动将产生教育内容（包括代码和视频教程），以及一个用户友好的应用程序，小型企业和高中生可以在不确定性下利用设计优化。这些活动将集体向学生和从业人员展示表明工程设计和机器学习可以大大提高我们在解决复杂工程问题方面的能力。这项研究的假设是，通过适当的转换操作机构和学习机制，可以通过一组低维和可解释的流形的对象来编码组合的组合设计空间和相关的不确定性，这是一个高度的代表，这是一个高高的代表。不确定性来源）。该假设将在四个研究推力中进行检验，以做出以下贡献：（1）开发一种不确定性表示方法，该方法提高了不确定性数量能力，还提供了可解释的可解释的诊断措施，以检测模型形成模型误差和非高斯不确定性的存在； （2）建立一种设计表示方法，用于在紧凑的歧管中编码庞大的组合设计空间，设计师可以利用该方法来识别有希望的组合； （3）引入概率的元模型和资源分配的新的最优指标； （4）开发一个多保真多尺度建模框架，该框架可以使基于均质化的多尺度模拟动态并自动调整嵌套模拟的保真度（因此，成本）和校准参数。这些方法上的贡献是通用的，可以使广泛的应用受益，例如多学科系统分析。该项目的教育和外展组成部分包括开发设计优化和校准应用程序，可以由高中生，教育工作者和当地的小型企业使用；将材料优化的技术转移到制造业；并开发教育材料和夏季研讨会系列。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估，被认为是珍贵的支持。