Fig. 1
From: Geometry aware physics informed neural network surrogate for solving Navier–Stokes equation (GAPINN)
Schematic description of the architecture of the proposed DNN Method (GAPINN) to generate surrogates of PDEs with irregular non-parameterized geometries using PINNs. The network consists of three subnetworks which are trained separately. The SEN is a Variational-Auto-Encoder type reducing the geometry dimensions to a latent vector k. PINN takes k and spatial positions to solve the PDE and building the surrogate and BCN, by also taking spatial information and k, helps constraining boundary conditions in the PINN. Dimensions at each operation are noted in brackets