| CAS Key Laboratory of Theoretical Physics |
| Institute of Theoretical Physics |
| Chinese Academy of Sciences |
| Lunch Seminar |
| Title 题目 | Machine Learning Application in Supersymmetry |
| Speaker 报告人 | 杨金民 |
| Affiliation 所在单位 | ITP |
| Date 日期 | 2017年10月31日（周 二）中午12：00 |
| Venue 地点 | Conference Hall 322, ITP/理论物理所322报告厅 |
| Abstract 摘要 | Will talk about a Machine Learning approach for a fast and reliable exploration of high dimensional parameter space by using machine learning models to evaluate the quality of random parameter sets. As a proof-of-concept, this approach is applied to several benchmark models including a supersymmetry scenario. The finding is that such an approach can significantly reduce the computational cost and ensure the discovery of all survived regions. |
| Contact Person 所内联系人 | 杨刚 |
