SACE: Statistics-Aided Computer Experiments

SACE：统计辅助计算机实验

• 批准号: 0620259
• 负责人: C. F. Jeff Wu
• 金额: $ 30万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-15 至 2010-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0620259&HistoricalAwards=false
• 关键词: SACE; Statistics; Aided; Computer; Experiments

With advances in computing, complex mathematical models implemented in large computer codes have become effective in describing physical processes and real systems. Many industries have made extensive use of computer models to replace physical experiments and seen the benefits in reduced development cycle time and cost. In this proposal a combination of statistical and engineering ideas will be used to develop novel methodologies and techniques for the planning of computer experiments, their modeling and analysis. A Bayesian approach will be developed to integrate data from two sources: high-accuracy but expensive experiments and low-accuracy but cheaper experiments. Since the performance of a system or product can be severely affected by its noise variations, system robustness needs to be considered before performing optimization. New techniques in robust design and optimization will be developed to handle problems with many noise factors and to address multiple performance measures. Specific applications to cellular alloy material design and data center thermal distribution will be considered for illustrating and testing the proposed methodology. Broader applications include manufacturing, material, environmental monitoring, hydrology, and impact dynamics.

随着计算技术的进步，在大型计算机代码中实现的复杂数学模型在描述物理过程和真实的系统方面已经变得有效。许多行业已经广泛使用计算机模型来取代物理实验，并看到了减少开发周期和成本的好处。在本提案中，统计和工程思想的结合将用于开发新的方法和技术，用于计算机实验的规划，建模和分析。将开发一种贝叶斯方法来整合两个来源的数据：高精度但昂贵的实验和低精度但便宜的实验。由于系统或产品的性能可能会受到其噪声变化的严重影响，因此在执行优化之前需要考虑系统的鲁棒性。将开发鲁棒设计和优化的新技术，以处理具有许多噪声因素的问题，并解决多个性能指标。蜂窝合金材料设计和数据中心的热分布的具体应用将被认为是说明和测试所提出的方法。更广泛的应用包括制造，材料，环境监测，水文和冲击动力学。