Boundary-Layer-Induced Failure of Standard Physics-Informed Neural Networks: A Legendre Wavelet Collocation Benchmark for Singularly Perturbed Transport Problems

Abstract

Boundary layers provide a demanding test for numerical solvers because the solution may remain almost constant over most of the domain while changing rapidly in a narrow region near the boundary. This paper studies a singularly perturbed one-dimensional transport boundary-value problem with increasing Peclet number $(\mathrm{Pe})$. A local Legendre wavelet collocation method (LWM) is compared with a standard soft-boundary physics-informed neural network (PINN) for this benchmark. The wavelet approximation uses locally supported Legendre polynomial basis functions and converts the problem into a square algebraic collocation system with residual, boundary, and interface-continuity equations. Numerical experiments are performed for $\mathrm{Pe}=1,10,100,$ and $1000$. The LWM captures all four cases, with the largest error remaining below $5\times 10^{-3}$. The standard soft-boundary PINN performs well for the mild cases but fails to resolve the sharp boundary layer for the larger Peclet numbers. The results show that local wavelet collocation is more reliable than the standard soft-boundary PINN for this benchmark, while dense near-boundary evaluation helps reveal errors that may be missed on coarse grids.