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CAREER: Multifidelity Modeling and Search Using Adaptive Field Prediction

职业：使用自适应场预测进行多保真度建模和搜索

基本信息

批准号：
2223732
负责人：
Leifur Leifsson
金额：
$ 50万
依托单位：
Purdue University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223732&HistoricalAwards=false
关键词：
CAREER Multifidelity Modeling Search Using

项目摘要

A variety of high-fidelity simulations are available to engineers in support the analysis and optimization of engineered systems. However, the computational demands of these simulation codes often mean advanced computational design techniques, such as uncertainty analysis and optimization under uncertainty, are not used to their fullest potential. This Faculty Early Career Development Program (CAREER) project supports fundamental research to advance techniques for simulation-based engineering systems design with a goal of making their application practical on a wider variety of important engineering problems. The project will result in new understanding about how engineers can utilize rich information from simulation results to accelerate computational applications such as uncertainty analysis and optimal design under uncertainty. The techniques target engineering applications that exhibit complex physics, such as aerodynamics, electromagnetics and mechanical structures. New methods pioneered in this project will impact society through more rapid and reliable design of complex engineered systems across domains such as transportation, energy harvesting, weather forecasting, and communication. Educational initiatives of this project focus on instruction and curriculum development for advanced computational design techniques. This includes a new short course on computational design for undergraduate students at Iowa State University, creation of an online hub to make advanced simulation-based design techniques accessible to students and practitioners around the country, and organization of mini-symposia on computational design.This research pioneers a novel class of methods for using the field responses of simulations to construct improved multifidelity models in the context of advanced computational design techniques such as uncertainty analysis and optimization under uncertainty. The process of extracting and adapting physics-based information encoded in the field responses of models of varying degrees of fidelity will be achieved by combining metamodeling techniques and machine learning, as well as the development of novel adaptation techniques. The new methods and algorithms will be derived and rigorously characterized through computational experiments with structural, electronic, and fluid systems case studies. Additionally, the results will provide an understanding of the impact of model correlations and the mechanisms controlling the growth of the computational cost. This will enable the creation of new and unique methods for the automated setup of multifidelity models and allow us to address problems of higher complexity than what is currently possible.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.

各种高保真仿真可供工程师在支持工程系统的分析和优化。然而，这些模拟代码的计算需求往往意味着先进的计算设计技术，如不确定性分析和不确定性下的优化，没有充分发挥其潜力。该学院早期职业发展计划（Career）项目支持基础研究，以推进基于仿真的工程系统设计技术，目标是将其应用于更广泛的重要工程问题。该项目将使工程师对如何利用模拟结果中的丰富信息来加速不确定性分析和不确定性下的优化设计等计算应用有新的认识。这些技术的目标是展示复杂物理的工程应用，如空气动力学、电磁学和机械结构。该项目开创的新方法将通过更快、更可靠地设计跨领域的复杂工程系统，如交通、能源收集、天气预报和通信，对社会产生影响。本计画的教育计划集中于先进计算设计技术的教学与课程发展。这包括为爱荷华州立大学的本科生开设一门新的计算设计短期课程，创建一个在线中心，使全国各地的学生和从业人员都可以使用基于模拟的高级设计技术，以及组织关于计算设计的小型专题讨论会。本研究开创了一种新的方法，在不确定性分析和不确定性优化等先进计算设计技术的背景下，利用模拟的现场响应来构建改进的多保真度模型。将元建模技术和机器学习相结合，以及开发新的适应技术，提取和适应不同保真度模型的现场响应中编码的基于物理的信息。新的方法和算法将通过结构、电子和流体系统案例研究的计算实验来推导和严格表征。此外，研究结果将有助于理解模型相关性的影响以及控制计算成本增长的机制。这将为多保真模型的自动设置创造新的独特方法，并使我们能够解决比目前可能的更复杂的问题。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。