MPIPN: A Multi Physics-Informed PointNet for solving parametric acoustic-structure systems

arXiv:2403.01132v1 Announce Type: new
Abstract: Machine learning is employed for solving physical systems governed by general nonlinear partial differential equations (PDEs). However, complex multi-physics systems such as acoustic-structure coupling are often described by a series of PDEs that incorporate variable physical quantities, which are referred to as parametric systems. There are lack of strategies for solving parametric systems governed by PDEs that involve explicit and implicit quantities. In this paper, a deep learning-based Multi Physics-Informed PointNet (MPIPN) is proposed for …

abstract arxiv cs.lg cs.sd differential eess.as general machine machine learning parametric physics physics-informed series systems type