Extraction of laws of nature by merging physical modeling and sparse modeling

2017 Fiscal Year Final Research Report

• Project Area: Initiative for High-Dimensional Data-Driven Science through Deepening of Sparse Modeling
• Project/Area Number: 25120010
• Research Category: Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
• Allocation Type: Single-year Grants
• Review Section: Complex systems
• Research Institution: The University of Tokyo
• Principal Investigator: Hukushima Koji 東京大学, 大学院総合文化研究科, 教授 (80282606)
• Co-Investigator(Kenkyū-buntansha): 大森 敏明 神戸大学, 工学研究科, 准教授 (10391898)
• Co-Investigator(Renkei-kenkyūsha): NAKANISHI Yoshinnori 東京大学, 大学院総合文化研究科, 助教 (00767296) ; TODO Shinji 東京大学, 理学系研究科, 准教授 (10291337)
• Project Period (FY): 2013-06-28 – 2018-03-31
• Keywords: データ駆動科学 / スパースモデリング / ベイズ推論

Outline of Final Research Achievements

Recent technological developments in experiments and measurements, which have made it possible to obtain high-dimensional data, mean it is no longer easy to organize natural phenomena. The purpose of our research project is to establish a universal modeling principle driven by spatiotemporal data for the extraction of laws of nature by applying sparse modeling. To do this, we conducted research mainly focusing on the three research subjects, as typical examples, which are scanning tunneling microscopy/spectrum in condensed matter physics, calcium imaging in neuroscience and dynamics of petrogenesis in earth science. They correspond to the extraction of laws of spatial structure formation, that of dynamics of time evolution, and that of spatiotemporal dynamics, respectively. Through the present research with the help of sparse modeling and Bayesian inference, we established a guiding principle of modeling in natural science independent of some details of research fields.

Free Research Field

統計物理学