Machine-Learning methods for fluid and fluid-structure interaction problems
Numerical simulation of fluid and fluid-structure interaction plays an essential role in modelling many physical phenomena. However, the computational cost limit the use of the numerical solvers in particular when small space and time features are required. Machine learning (ML) techniques can be used to improve approximations inside computational frameworks to enhance their predictivity capability. In this work, different ML techniques will be coupled with existing solvers to test the best solution in terms of accuracy and efficiency.