Open AccessEnhanced data assimilation of 4D LPT with physics informed neural networks
Author(s) -
Ji-Young Han,
Dong Kim,
Hyungmin Shin,
Kyung Chun Kim
Publication year - 2021
Publication title -
international symposium on particle image velocimetry.
Language(s) - English
Resource type - Journals
ISSN - 2769-7576
DOI - 10.18409/ispiv.v1i1.123
Subject(s) - artificial neural network , constraint (computer aided design) , computation , explosive material , computer science , physical system , meaning (existential) , field (mathematics) , artificial intelligence , data assimilation , physical science , mathematics , physics , algorithm , engineering , mathematics education , psychology , quantum mechanics , mechanical engineering , meteorology , chemistry , organic chemistry , pure mathematics , psychotherapist
According to recent trend of explosive growth of computation power and accumulated data, demand for the deep learning application in various research fields is increasing. As following this trend, remarkable achievements are presented in the experimental fluid mechanics field. One of the most outstanding research is Physics Informed Neural Networks (PINN) Raissi et al. (2020). Physical knowledge, which has been accumulated by humans, is imposed on the neural networks. PINN was used the automatic differentiation for implementing the governing equations as a physical constraint. By utilizing this concept, physical constraints make neural networks finding physical meaning of phenomena instead of simply fitting to the label data.