SACE: Statistics-Aided Computer Experiments

随着计算机技术的进步，在大型计算机代码中实现的复杂数学模型已经成为描述物理过程和实际系统的有效方法。许多行业已经广泛使用计算机模型来取代物理实验，并看到了缩短开发周期时间和成本的好处。在这个建议中，统计和工程思想的结合将被用来开发新的方法和技术来规划计算机实验，他们的建模和分析。将开发贝叶斯方法来整合来自两个来源的数据：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会受到噪声变化的严重影响，因此在进行优化之前需要考虑系统的鲁棒性。稳健设计和优化方面的新技术将被开发出来，以处理具有许多噪声因素的问题，并解决多种性能指标。将考虑蜂窝合金材料设计和数据中心热分布的具体应用，以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。

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.