Two-Phase Optimization for PINN Training

Abstract

This work presents an algorithm for training Neural Networks where the loss function can be decomposed into two non-negative terms to be minimized. The proposed method is an adaptation of Inexact Restoration algorithms, constituting a two-phase method that imposes descent conditions. Some performance tests are carried out in PINN training.