Hi, this is very nice work! I understand MeshCNN is designed to represent 3d objects. I am wondering whether it could be adapted to train a PDE forward emulator where the inputs are discrete parameter (e.g. PDE coefficient) values over (2d/3d) finite element mesh and outputs are the PDE solution evaluated at certain points. It is more like a regression problem defined on FEM space. Particularly, if the physical domain is 2d, is it possible to adapt MeshCNN for such application? Thanks!
Hi, this is very nice work! I understand MeshCNN is designed to represent 3d objects. I am wondering whether it could be adapted to train a PDE forward emulator where the inputs are discrete parameter (e.g. PDE coefficient) values over (2d/3d) finite element mesh and outputs are the PDE solution evaluated at certain points. It is more like a regression problem defined on FEM space. Particularly, if the physical domain is 2d, is it possible to adapt MeshCNN for such application? Thanks!