Gated X-TFC: Soft domain decomposition for forward and inverse problems in sharp-gradient PDEs

Abstract

Physics-informed neural networks (PINNs) and related methods struggle to resolve sharp gradients in singularly perturbed boundary value problems without resorting to some form of domain decomposition, which often introduces complex interface penalties. While the Extreme Theory of Functional Connections (X-TFC), thanks to the TFC component, avoids multi-objective optimization by employing exact boundary condition enforcement, it remains computationally inefficient for boundary layers and incompatible with decomposition. We propose Gated X-TFC, a novel framework for both forward and inverse problems, that overcomes these limitations through a soft, learned domain decomposition. Our method replaces hard interfaces with a differentiable logistic gate that dynamically adapts radial basis function (RBF) kernel widths across the domain, eliminating the need for interface penalties. This approach yields not only superior accuracy but also significant improvements in computational efficiency: on a benchmark one dimensional (1D) convection–diffusion, Gated X-TFC achieves an order-of-magnitude lower error than standard X-TFC while using 80% fewer collocation points and reducing training time by 66%. In addition, we introduce an operator-conditioned meta-learning layer that learns a probabilistic mapping from PDE parameters to optimal gate configurations, enabling fast, uncertainty-aware warm-starting for new problem instances. We further demonstrate extensibility to multiple subdomains and higher dimensions by solving a twin boundary–layer equation and a 2D Poisson problem with a sharp Gaussian source. Overall, Gated X-TFC delivers a simple alternative to PINNs that is both accurate and computationally efficient for challenging boundary-layer regimes. Future work will focus on nonlinear problems. For reproducibility, all the codes are available at https://github.com/vikas-dwivedi-2022/gated_xtfc