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Neural Harmonic Measure Operator

Paper recorded by Signals 4 on 2026-09-28 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-28 on arXiv · recorded by Signals 4 on 2026-09-29

Category: cs.LG · 机器学习 · first seen 2026-09-29

Abstract

We introduce Neural Harmonic Measure Operator (NHMO), a neural solver for elliptic PDE problems on variable-shape domains. The harmonic measure of a domain is the boundary probability distribution that, integrated against any boundary data, returns the Dirichlet Laplace solution. It depends only on the geometry, not on the boundary data. NHMO parameterizes the density of this measure as a transfor

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#4 most recent of 322 cs.LG papers we have recorded · ↑ newer: Statistical Learning of Contractive Dynamical Representations for Comp · ↓ older: Improving Test-Time Scaling with Adaptive Looped Transformers
Cite this page: Neural Harmonic Measure Operator: the #4 most recent of 322 cs.LG papers we have recorded (as of 2026-09-28). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/neural-harmonic-measure-operator.html
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