Physics Informed Neural Networks for Nonlinear Delay Differential Equations

In this paper we propose a novel physics-informed neural network framework for solving general first-order delay differential equations.

Our approach combines a differentiable history switch, a trial-solution formulation that explicitly enforces history constraints, and a segmented collocation strategy to stabilize gradient propagation across large temporal domains.

The method enables a scalable and physics-consistent approximation of delay differential equation solutions while maintaining continuity across subintervals.

Numerical experiments demonstrate the effectiveness of the proposed method.