Immersed boundary-physics informed machine learning approach for fluid-solid

Fluid-solid coupling is commonly used but sometimes expensive in large-scale simulations for fluid dynamics. Conventional numerical methods rely on high performance computers and parallel computing techniques to accelerate simulations. In this work, a lightweight immersed boundary-physics informed machine learning model is proposed for the fluid-solid coupling based on the physical framework of multi-direct forcing of the immersed boundary method. Two dimensional flows past a static cylinder are adopted as case studies for the drag. It shows close agreements of drag coefficient among simulations conducted by the immersed boundary -lattice Boltzmann method (IB-LBM), immersed boundary-physics informed neural network model (IB-PINN), and data from references. No-slip boundary conditions around the cylinder boundaries are closely satisfied and the time consumed by the machine learning model is reduced by 38.5% compared with IB-LBM, which demonstrates that the machine learning approach is robust, fast, and accurate.