Extended Interface Physics-Informed Neural Networks Method for Moving Interface Problems
For researchers solving moving interface PDEs, this method extends PINNs to handle dynamic interfaces, but the contribution is incremental as it combines existing techniques.
The paper introduces the XI-PINN framework for solving parabolic moving interface problems, using a level set function to capture the interface. Numerical experiments validate accuracy and robustness, with application to Oseen equations.
Physics-informed neural networks (PINNs) have emerged as an effective class of mesh-free methods for solving partial differential equations (PDEs), particularly on complex geometries. In this paper, we introduce an Extended Interface Physics-Informed Neural Network (XI-PINN) framework designed to solve parabolic moving interface problems. The proposed method employs a level set function--which can be either analytically prescribed or learned via a neural network--to capture the moving interface. Furthermore, we establish an a priori error analysis for the XI-PINN method and derive error bounds for the approximation. Extensive numerical experiments are provided to validate the accuracy and robustness of the framework, and its applicability is further demonstrated by solving the Oseen equations.