Paradigm Shift Through the Integration of Physical Methodology and Data Science
This work provides a theoretical overview for researchers in scientific fields facing interpretability and prediction issues, but it is incremental as it surveys existing integrated methods.
The paper addresses the challenges of interpretability and extrapolative predictions in data science by integrating traditional physical methodologies, highlighting their significance and surveying current methods and applications.
Data science methodologies, which have undergone significant developments recently, provide flexible representational performance and fast computational means to address the challenges faced by traditional scientific methodologies while revealing unprecedented challenges such as the interpretability of computations and the demand for extrapolative predictions on the amount of data. Methods that integrate traditional physical and data science methodologies are new methods of mathematical analysis that complement both methodologies and are being studied in various scientific fields. This paper highlights the significance and importance of such integrated methods from the viewpoint of scientific theory. Additionally, a comprehensive survey of specific methods and applications are conducted, and the current state of the art in relevant research fields are summarized.