Paradigm Shift Through the Integration of Physical Methodology and Data Science

TL;DR

This paper discusses the integration of traditional physical methodologies with modern data science techniques, emphasizing its significance for scientific theory and providing a comprehensive survey of current methods and applications.

Contribution

It highlights the importance of integrated physical and data science methods and offers a comprehensive survey of their current state and applications.

Findings

Integrated methods address interpretability and extrapolation challenges.

Survey of various applications across scientific fields.

Current research trends and future directions summarized.

Abstract

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.