Knowledge Integration into deep learning in dynamical systems: an overview and taxonomy

• Published in: Journal of mechanical science and technology Vol. 35; no. 4; pp. 1331 - 1342
• Main Authors: Kim, Sung Wook, Kim, Iljeok, Lee, Jonghwan, Lee, Seungchul
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society of Mechanical Engineers 01.04.2021; Springer Nature B.V; 대한기계학회
• Subjects: Artificial neural networks; Control; Deep learning; Dynamical Systems; Engineering; Industrial and Production Engineering; Invited Review Article; Literature reviews; Machine learning; Mechanical Engineering; Neural networks; Taxonomy; Vibration; 기계공학; Deep neural networks; Taxonomy; Knowledge integration; Knowledge representation; Informed deep learning; Physics-informed; Dynamical system
• DOI: 10.1007/s12206-021-0342-5
• ISSN: 1738-494X

Despite the sudden rise of AI, it still leaves a question mark to many newcomers on its widespread adoption as it exhibits a lack of robustness and interpretability. For instance, the insufficient amount of training data usually hinders its performance due to the lack of generalization, and the black box nature of deep neural networks does not allow for a precise explanation behind its mechanism preventing a new scientific discovery. Such limitations have led to the development of several branches of deep learning one of which include physics-informed neural networks that will be covered in the rest of this paper. In this overview, we defined the general concept of informed deep learning followed by an extensive literature survey in the field of dynamical systems. We hope to make a contribution to our mechanical engineering community by conveying knowledge and insights on this emerging field of study through this survey paper.