Collaborative Research: Validation, Calibration, and Prediction of Computer Models with Functional Output

协作研究：具有功能输出的计算机模型的验证、校准和预测

• 批准号: 0927572
• 负责人: Ying Hung
• 金额: $ 11.25万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0927572&HistoricalAwards=false
• 关键词: Collaborative; Research; Validation; Calibration; Prediction

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The current research in Bayesian model prediction and validation of computer models mainly focuses on computer experiments with single output and fixed input variables. The proposed research focuses on Bayesian approach for calibration, validation, prediction, and experimental design of computer models with functional output. Bayesian predictive models for calibrating computer models based on functional computer outputs and physical observations will be constructed. Methods and metrics for calibrating and validating computer models will be developed. Experimental design and optimization strategies for data collection will be established. Theoretical properties of the developed methodologies will be investigated and assessed.If successful, the research results will bridge the gap between statistical researchers and engineering practitioners, and stimulate additional research that improve effective and efficient utilization of expensive computer models developed by scientists and engineers. There is an increasing demand for accurate predictive models and metrics for calibrating and validating computer models from model analysts and engineering designers. The proposed research will allow scientists and engineers to effectively assess and evaluate expensive computer models for various scientific and engineering applications, including IC packaging and fabrication, chemical and nuclear energy equipment development, cellular material design and manufacturing, nano material design and manufacturing, etc. The major impact of the proposed research is to improve the effectiveness of computer model developers (model analysts) and users (scientists and engineering designers) in scientific understanding as well as in design and manufacturing in various important scientific and engineering applications.

该奖项是根据2009年美国复苏和再投资法案(公法111-5)资助的。目前在贝叶斯模型预测和计算机模型验证方面的研究主要集中在单输出和固定输入变量的计算机实验。建议的研究重点是贝叶斯方法，用于校准、验证、预测和设计具有功能输出的计算机模型。将建立贝叶斯预测模型，用于根据功能计算机输出和物理观测来校准计算机模型。将制定校准和验证计算机模型的方法和指标。将制定数据收集的实验设计和优化策略。研究将对所开发方法的理论性质进行调查和评估。如果成功，研究结果将弥合统计研究人员和工程从业者之间的差距，并刺激更多的研究，以提高对科学家和工程师开发的昂贵计算机模型的有效和高效利用。模型分析人员和工程设计人员对用于校准和验证计算机模型的准确预测模型和度量的需求越来越大。拟议的研究将使科学家和工程师能够有效地评估和评估用于各种科学和工程应用的昂贵的计算机模型，包括IC封装和制造、化学和核能设备开发、蜂窝材料设计和制造、纳米材料设计和制造等。拟议研究的主要影响是提高计算机模型开发者(模型分析师)和用户(科学家和工程设计人员)在科学理解以及各种重要科学和工程应用的设计和制造方面的有效性。