Late-breaking Work · 4th World Conference on eXplainable AI (xAI 2026)
We propose a post-hoc method for estimating pointwise errors of Physics-Informed Neural Networks by exploiting the fact that, for linear PDEs, the error satisfies the same differential operator driven by the PINN residual. This yields spatially resolved error maps — interpretable diagnostics analogous to attribution maps in XAI.