A study of Physics-Informed Deep Learning Approach to Solve 2D Hyperbolic-Transport Models

Aluno: Rafael Carniello

The recent development of physics-informed deep learning models (e.g., [1]) to numerically approximate the solution of differential equations represents an advance for solving scientific computing and engineering problems, which the lack of available data is a real fact due to several limitations to access information and challenges in real world. It also seems to be promising for improving the PDE modeling,