Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data
This addresses the problem of enhancing scientific discovery through data-driven methods for researchers in fields such as physics and biology, though it is conceptual and incremental in formalizing an emerging approach.
The paper tackles the limited applicability of data science models in scientific domains by proposing Theory-guided Data Science (TGDS), a new paradigm that integrates scientific knowledge to improve model effectiveness and interpretability, with examples across disciplines like turbulence modeling and climate science.
Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery. The overarching vision of TGDS is to introduce scientific consistency as an essential component for learning generalizable models. Further, by producing scientifically interpretable models, TGDS aims to advance our scientific understanding by discovering novel domain insights. Indeed, the paradigm of TGDS has started to gain prominence in a number of scientific disciplines such as turbulence modeling, material discovery, quantum chemistry, bio-medical science, bio-marker discovery, climate science, and hydrology. In this paper, we formally conceptualize the paradigm of TGDS and present a taxonomy of research themes in TGDS. We describe several approaches for integrating domain knowledge in different research themes using illustrative examples from different disciplines. We also highlight some of the promising avenues of novel research for realizing the full potential of theory-guided data science.