2021/09/30 by Takashi Miyamoto, Miyamoto, Takashi
Computer Science · Decision Sciences · Engineering · Physics and Astronomy · #Artificial intelligence #Complement (music) #Computational Physics (physics.comp-ph) #Computational Physics and Python Applications #Computer science #Data Analysis #Data science #Engineering #Epistemology #FOS: Computer and information sciences #FOS: Physical sciences #I.5.1 #I.6.0 #Interpretability #J.2 #Machine Learning (cs.LG) #Management science #Model Reduction and Neural Networks #Paradigm shift #Physical science #Probabilistic and Robust Engineering Design #Social science #Sociology #Statistics and Probability (physics.data-an) #cs.LG #physics.comp-ph #physics.data-an
paper · pdf · doi:10.48550/arxiv.2110.01408
published in arXiv (Cornell University) (Cornell University) · 15 pages, 7 figures
arxiv created 2021/09/30 · openalex publication_date 2021/09/30 · arxiv updated 2021/10/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
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.