English

Comparing EPGP Surrogates and Finite Elements Under Degree-of-Freedom Parity

Machine Learning 2025-11-07 v1 Numerical Analysis Numerical Analysis Machine Learning

Abstract

We present a new benchmarking study comparing a boundary-constrained Ehrenpreis--Palamodov Gaussian Process (B-EPGP) surrogate with a classical finite element method combined with Crank--Nicolson time stepping (CN-FEM) for solving the two-dimensional wave equation with homogeneous Dirichlet boundary conditions. The B-EPGP construction leverages exponential-polynomial bases derived from the characteristic variety to enforce the PDE and boundary conditions exactly and employs penalized least squares to estimate the coefficients. To ensure fairness across paradigms, we introduce a degrees-of-freedom (DoF) matching protocol. Under matched DoF, B-EPGP consistently attains lower space-time L2L^2-error and maximum-in-time L2L^{2}-error in space than CN-FEM, improving accuracy by roughly two orders of magnitude.

Keywords

Cite

@article{arxiv.2511.04518,
  title  = {Comparing EPGP Surrogates and Finite Elements Under Degree-of-Freedom Parity},
  author = {Obed Amo and Samit Ghosh and Markus Lange-Hegermann and Bogdan Raiţă and Michael Pokojovy},
  journal= {arXiv preprint arXiv:2511.04518},
  year   = {2025}
}

Comments

14 pages, 2 figures