Continuous Data Assimilation for the Navier-Stokes Equations with Nonlinear Slip Boundary Conditions
This paper focuses on continuous data assimilation (CDA) for the Navier-Stokes equations with nonlinear slip boundary conditions. CDA methods are typically employed to recover the original system when initial data or viscosity coefficients are unknown, by incorporating a feedback control term generated by observational data over a time period. In this study, based on a regularized form derived from the variational inequalities of the Navier-Stokes equations with nonlinear slip boundary conditions, we first investigate the classical CDA problem when initial data is absent. After establishing the existence, uniqueness and regularity of the solution, we prove its exponential convergence with respect to the time. Additionally, we extend the CDA to address the problem of missing viscosity coefficients and analyze its convergence order, too. Furthermore, utilizing the predictive capabilities of partial evolutionary tensor neural networks (pETNNs) for time-dependent problems, we propose a novel CDA by replacing observational data with predictions got by pETNNs. Compared with the classical CDA, the new one can achieve similar approximation accuracy but need much less computational cost. Some numerical experiments are presented, which not only validate the theoretical results, but also demonstrate the efficiency of the CDA.