CAREER: Multifidelity Modeling and Search Using Adaptive Field Prediction

工程师可以使用各种高保真模拟来支持工程系统的分析和优化。然而，这些仿真代码的计算需求往往意味着先进的计算设计技术，如不确定分析和不确定条件下的优化，没有得到最大限度的发挥。这一学院早期职业发展计划(CALEAR)项目支持基础研究，以促进基于模拟的工程系统设计技术的进步，目标是使其在更广泛的重要工程问题上得到实际应用。该项目将对工程师如何利用模拟结果中的丰富信息来加速计算应用程序(如不确定性分析和不确定条件下的优化设计)产生新的理解。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速、更可靠地设计跨越交通、能源收集、天气预报和通信等领域的复杂工程系统来影响社会。该项目的教育举措侧重于先进计算设计技术的教学和课程开发。这包括在爱荷华州立大学为本科生开设一个新的计算设计短期课程，创建一个在线中心，使全国的学生和从业者能够接触到基于模拟的先进设计技术，并组织关于计算设计的小型研讨会。这项研究开创了一类新的方法，用于在不确定情况下的不确定性分析和优化等高级计算设计技术的背景下，使用模拟的现场响应来构建改进的多保真模型。通过结合元建模技术和机器学习以及新的适应技术的开发，将实现提取和适应在不同保真度模型的现场响应中编码的基于物理的信息的过程。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进行推导和严格表征。此外，这些结果将提供对模型相关性的影响以及控制计算成本增长的机制的理解。这将使我们能够创建新的和独特的方法来自动设置多保真模型，并允许我们解决比目前可能的更复杂的问题。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。

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.