Robust training approach of neural networks for fluid flow state estimations

Understand

State estimation from limited sensor measurements is ubiquitously found as a common challenge in a broad range of fields including mechanics, astronomy, and geophysics.

• Fluid mechanics is no exception -- state estimation of fluid flows is particularly important for flow control and processing of experimental data.
• However, strong nonlinearities and spatio-temporal high degrees of freedom of fluid flows cause difficulties in reasonable estimations.
• To handle these issues, neural networks (NNs) have recently been applied to the fluid flow estimation instead of conventional linear methods.