Constructs symmetric analytic positivity-preserving Ornstein-Uhlenbeck semigroups on rooted metric trees with Gaussian measures, establishes compactness and eigenvalue asymptotics, and reduces regular cases to half-line problems via adapted Naimark-Solomyak decomposition.
ORNSTEIN-UHLENBECK ON TREES 25
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Monte Carlo estimation of volumetric Steklov operators enables robust spectral geometry processing at the scale of hundreds of thousands of in-the-wild meshes and supports contrastive 3D representation learning.
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Ornstein--Uhlenbeck semigroup on rooted trees
Constructs symmetric analytic positivity-preserving Ornstein-Uhlenbeck semigroups on rooted metric trees with Gaussian measures, establishes compactness and eigenvalue asymptotics, and reduces regular cases to half-line problems via adapted Naimark-Solomyak decomposition.
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Monte Carlo Steklov Operators for Large-Scale Geometry Processing in the Wild
Monte Carlo estimation of volumetric Steklov operators enables robust spectral geometry processing at the scale of hundreds of thousands of in-the-wild meshes and supports contrastive 3D representation learning.