NHMO enables fast, geometry-aware PDE solving by learning a domain-specific kernel once, then reusing it for any boundary conditions or sources—eliminating expensive retraining for each new problem.
This paper presents Neural Harmonic Measure Operator (NHMO), a neural network approach that solves elliptic PDEs (like Laplace and Poisson equations) on domains with varying shapes.