Enhanced data assimilation of 4D LPT with physics informed neural networks

Authors

  • Jeongmin Han Pusan National University, Korea, Republic of (South Korea)
  • Dong Kim Pusan National University, Korea, Republic of (South Korea)
  • Hyungmin Shin Pusan National University, Korea, Republic of (South Korea)
  • Kyung Chun Kim Pusan National University, Korea, Republic of (South Korea)

DOI:

https://doi.org/10.18409/ispiv.v1i1.123

Keywords:

Physics Informed Neural Networks, ANFIS, 4D LPT, data assimilation, physical constraint

Abstract

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.

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Published

2021-08-01

Issue

Section

Deep Learning and Data Assimilation