PINN for Dynamical Partial Differential Equations is Not Training Deeper Networks Rather Learning Advection and Time Variance

The concepts and techniques of physics-informed neural networks (PINNs) is studied and limitations are identified to make it efficient to approximate dynamical equations. Potential working research domains are explored for increasing the robustness of this technique for the solvability of partial differential equations. It is identified that PINNs potentially fails to stronger advection and longer time duration. Also, optimization function and constraint posing needs to be smarter. Even a shallow network is good for a lot of problems while powerful deeper network fails. Reservoir computing based recurrent neural network architecture is recommended to solve dynamical problems.