PARADIGM SHIFT THROUGH THE INTEGRATION OF PHYSICAL METHODOLOGY AND DATA SCIENCE

Abstract

Data science methodologies, which have undergone significant development in recent years, provide flexible representational performance and fast computational means to address the challenges faced by traditional scientific methodologies while simultaneously revealing unprecedented challenges such as the interpretability of computations and the demand for extrapolative predictions on the number of data. Methods that integrate traditional physical and data science methodologies are new methods of mathematical analysis that make complementary use of both methodologies and are being studied in a variety of scientific fields. In this paper, I point out the significance and importance of such integrated methods from the viewpoint of scientific theory, and survey and systematize specific methods and applications, and summarize the current knowledge in the relevant research fields.