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

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.