题　目：Statistical Surrogates: How Statistics May Impact High Performance Computing 主讲人：王伟仲教授（台湾大学数学系） 时　间：101年4月13日(星期五)下午14:00-14:50 地　点：交大综合一馆427室 Abstract Fast evolving computing technologies have kept bringing excellent opportunities to process a great amount of data. These technologies have also kept inducing novel and exciting scientific discoveries. However, it is quite a challenge to design efficient algorithms on these powerful computers due to their architecture complexities and the lack of equation-based performance models. To optimize the performance of computer algorithms or computer codes, it is common we have to tune the algorithms or codes via limited data. As such tasks can be modeled as data-driven optimization problems, performance surrogates constructed by statistical tools play a critical role in these computer experiments. We illustrate such approach by showing how design and analysis of computer experiments (DACE) and expected improvement (EI) can be applied to highly time consuming numerical simulation for three-dimensional photonic crystals shape optimization. The potential of statistical surrogates is far beyond the aforementioned description. Taking a new medical imaging methodology and a software auto-tuning framework as examples, we scratch some thoughts on a promising perspective that statistical surrogates is a key player for the latest and the next generation computers. --

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