CAREER: Model-Free Input Screening and Sensitivity Analysis in Simulation Metamodeling

职业：仿真元建模中的无模型输入筛选和敏感性分析

• 批准号: 1846663
• 负责人: Xi Chen
• 金额: $ 50万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1846663&HistoricalAwards=false
• 关键词: CAREER; Model; Free; Input; Screening

This Faculty Early Career Development Program (CAREER) award will contribute to national health and prosperity by improving modeling and analysis using large-scale simulations. Simulation is a widely used method to model stochastic systems, but analysis of these models becomes computationally and statistically difficult when the models involve a large number of potential input parameters. In order to improve model tractability, it becomes important to identify a subset of significant input parameters, and then to design effective simulation experiments using these input parameters. This project will provide new input screening and sensitivity analysis techniques for improving the decision-making capability within performance and schedule requirements in large-scale, complex systems applications. The techniques have the potential to apply to a wide range of application areas, such as biomedical studies, health care, manufacturing, and defense and homeland security operations. This project will also positively impact engineering education and broaden the participation of underrepresented groups in the engineering enterprise. The investigator will develop methods to enhance the scalability and sampling efficiency of metamodel-based simulation analysis. The key technical components include (1) a model-free method based on the Morris elementary effects method for sequential input screening with rigorous statistical performance guarantees, (2) theory and methods for constructing knowledge- and data-driven scalable heteroscedastic dual metamodels, and (3) a rigorous metamodel-based global sensitivity analysis approach to quantifying the impact of each active input under heteroscedasticity with finite-sample and large-sample performance guarantees. This project will generate novel methodology and practical algorithms for effective and efficient online input screening and global sensitivity analysis. The methods and algorithms developed will be tested on complex stochastic systems through two ongoing research collaborations with biomedical informatics researchers. The educational plan includes development of pre-college outreach programs, undergraduate and graduate curricula, and career development-related workshops for female engineering students.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.

该教师早期职业发展计划（职业）奖将通过使用大规模模拟来改进建模和分析来为国家健康和繁荣做出贡献。 仿真是一种广泛使用的方法来建模随机系统，但是当模型涉及大量潜在输入参数时，对这些模型的分析在计算和统计上很难。 为了提高模型障碍性，确定重要输入参数的子集，然后使用这些输入参数设计有效的仿真实验变得很重要。 该项目将提供新的输入筛选和敏感性分析技术，以提高大规模复杂系统应用中的性能和计划要求中的决策能力。 这些技术有可能应用于广泛的应用领域，例如生物医学研究，医疗保健，制造业以及国防和国土安全操作。 该项目还将对工程教育产生积极影响，并扩大代表性不足的团体在工程企业中的参与。研究者将开发方法来提高基于元模型的仿真分析的可扩展性和采样效率。 The key technical components include (1) a model-free method based on the Morris elementary effects method for sequential input screening with rigorous statistical performance guarantees, (2) theory and methods for constructing knowledge- and data-driven scalable heteroscedastic dual metamodels, and (3) a rigorous metamodel-based global sensitivity analysis approach to quantifying the impact of each active input under heteroscedasticity with finite-sample and large-sample performance保证。该项目将生成新颖的方法论和实用算法，以进行有效，有效的在线输入筛选和全球灵敏度分析。 开发的方法和算法将通过与生物医学信息学研究人员进行的两项正在进行的研究合作对复杂的随机系统进行测试。 该教育计划包括制定大学前宣传计划，本科和研究生课程以及针对女性工程专业学生的职业发展与职业发展有关的研讨会。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来进行评估的。

项目成果

Efficient and Robust Online Trajectory Prediction for Non-Cooperative Unmanned Aerial Vehicles

非合作无人机高效稳健的在线轨迹预测

• DOI: 10.2514/1.i010997
• 发表时间: 2021
• 期刊: Journal of Aerospace Information Systems
• 影响因子: 1.5
• 作者: Xie, Guangrui;Chen, Xi
• 通讯作者: Chen, Xi

Uniform Error Bounds for Stochastic Kriging

随机克里金法的统一误差界

• DOI: 10.1109/wsc48552.2020.9384009
• 发表时间: 2020
• 期刊: Winter Simulation Conference
• 作者: Xie, Guangrui;Chen, Xi
• 通讯作者: Chen, Xi

Empirical Uniform Bounds For Heteroscedastic Metamodeling

• DOI: 10.1109/wsc57314.2022.10015525
• 发表时间: 2022-12
• 期刊: 2022 Winter Simulation Conference (WSC)
• 作者: Yutong Zhang;Xi Chen

Distributed Variational Inference-Based Heteroscedastic Gaussian Process Metamodeling

基于分布式变分推理的异方差高斯过程元建模

• DOI: 10.1109/wsc40007.2019.9004911
• 发表时间: 2019
• 期刊: Proceedings of the Winter Simulation Conference
• 作者: Wang, Wenjing;Chen, Xi
• 通讯作者: Chen, Xi

Input Modeling and Uncertainty Quantification for Improving Volatile Residential Load Forecasting

• DOI: 10.1016/j.energy.2020.119007
• 发表时间: 2019-05
• 期刊: ArXiv
• 作者: Guangrui Xie;Xi Chen;Yang Weng