Distributed physics informed neural network for data-efficient solution to partial differential equations

Understand

The physics informed neural network (PINN) is evolving as a viable method to solve partial differential equations.

• In the recent past PINNs have been successfully tested and validated to find solutions to both linear and non-linear partial differential equations (PDEs).
• However, the literature lacks detailed investigation of PINNs in terms of their representation capability.
• In this work, we first test the original PINN method in terms of its capability to represent a complicated function.