|
|||
Venue | Period
POSTECH Information Research Laboratories Rm 122 | January 13 ~ 17, 2020
Overview
Statistical mechanics provides a powerful tool for understanding macroscopic systems on the basis of microscopic laws governing the dynamics of their constituents. Its ever-increasing use is not limited to physical sciences but extends to biological and social sciences. This annual event is intended to provide graduate students and early-career scientists in statistical physics and related disciplines with the conceptual framework and analytical tools for advanced studies in statistical physics and interdisciplinary research.
Topic
Thematic focus of the 17th School is on machine learning for statistical physics in practice. 15 hours of lectures by three invited lecturers will be devoted to learn, with emphasis in practice of concrete implementations, how the core concepts and skills of machine/deep learning can help studying statistical physics problems. Three lecture series (5 hours each) will be given under the tentative titles - Machine Learning in Practice (with SciKit-Learn & Keras) (IHL) - Machine Learning for Study of Phase Transitions (JWL) - Reservoir Computing for Study of Chaotic Systems (SWS) Organizers
Kwang-Il Goh (Korea University; kgoh at korea.ac.kr) Hyunggyu Park (KIAS) Soon-Hyung Yook (Kyunghee University) Jae Woo Lee (Inha University)
Lecturers In-Ho Lee (KRISS) Ji-Woo Lee (Myungji University) Seung-Woo Son (Hanyang University) Contacts Hyo-Eun Lee (hyoeun.lee at apctp.org) Jeongeun Yoon (avecyoon at kias.re.kr) Sponsors  |
|||