Multi-Task Learning Enhanced Physics-Informed Neural Network for Solving Fluid-Structure Interaction Equations

Physics-informed neural networks (PINNs) incorporate physical constraints into their loss functions, allowing them to efficiently solve Partial Differential Equations (PDEs). In this work, we introduce an innovative network structure based on PINNs, specifically designed to address fluid-structure interaction (FSI) problems in porous media, referred to as M-PINN. Our approach is inspired by fully coupled traditional numerical methods, leveraging a multi-task learning (MTL) framework with soft parameter sharing to enhance the interactions across different physical fields. This alignment leads to an improved representation of multi-physics coupling phenomena. Experiments on the constant pressure production problem demonstrate that our method provides better performance than both traditional and sequential PINN approaches.