基本信息
浏览量:18
职业迁徙
个人简介
Willcox's research has produced scalable computational methods for design of next-generation engineered systems, with a particular focus on model reduction as a way to learn principled approximations from data and on multifidelity formulations to leverage multiple sources of uncertain information. She currently has funded projects supported by the US Air Force Office of Scientific Research, Air Force Research Laboratory, ARPA-E, Department of Energy, Lockheed Martin, NASA, Sandia National Laboratories, and the Texas Higher Education Coordinating Board. Willcox currently leads several multi-institution research teams: she is Co-director of the Department of Energy AEOLUS Multifaceted Mathematics Capability Center on Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems; she leads an Air Force MURI team on Machine Learning for Physics-based Systems; and she leads the Rise of the Machines team developing robust, interpretable, scalable, efficient methods for digital twins under the Department of Energy AI and Decision Support for Complex Systems program.
研究兴趣
论文共 278 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
Karen Willcox, Brittany Segundo
Nature Computational Scienceno. 3 (2024): 147-149
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2024): 116584-116584
Computers & Structures (2024): 107328
ANNUAL REVIEW OF FLUID MECHANICSno. 1 (2024): 521-548
CHAOSno. 3 (2024)
Alberto Ferrari,Karen Willcox
Nature Computational Scienceno. 3 (2024): 178-183
AIAA SCITECH 2023 Forum (2023)
AIAA SCITECH 2023 Forum (2023)
加载更多
作者统计
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn