A forward-only Lanczos gradient approximation for Hermitian matrix function bilinear forms whose error scales with the same residual norm as the forward approximation and appears stable without reorthogonalization.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
S-SPH extends mesh-free SPH to stochastic problems via polynomial chaos and KL expansions, delivering mean and variance statistics that match Monte Carlo at up to 1000 times lower cost on benchmark advection and Burgers flows.
A Dirichlet process mixture model for marked Poisson point processes with squared-link intensities and Laplace variational inference jointly infers clusters, cluster count, and continuous mark-specific intensity surfaces.
citing papers explorer
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Fast and Stable Gradient Approximation for Bilinear Forms of Hermitian Matrix Functions
A forward-only Lanczos gradient approximation for Hermitian matrix function bilinear forms whose error scales with the same residual norm as the forward approximation and appears stable without reorthogonalization.
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Stochastic Smoothed Particle Hydrodynamics for Stochastic Mechanics Problems
S-SPH extends mesh-free SPH to stochastic problems via polynomial chaos and KL expansions, delivering mean and variance statistics that match Monte Carlo at up to 1000 times lower cost on benchmark advection and Burgers flows.
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Laplace Variational Inference for Dirichlet Process Mixtures of Marked Poisson Point Processes
A Dirichlet process mixture model for marked Poisson point processes with squared-link intensities and Laplace variational inference jointly infers clusters, cluster count, and continuous mark-specific intensity surfaces.