Impulse responses are cast as random atomic superpositions of stable poles inside a disk and recovered through constrained convex optimization that encodes engineering priors.
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3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Sobolev regularization on the witness function enables global convergence of MMD gradient flows for both sampling and generative modeling without isoperimetric assumptions.
UD-DML creates balanced representative subsamples via uniform design in PCA space for efficient double machine learning estimation of average treatment effects on large datasets.
citing papers explorer
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Randomized Atomic Feature Models for Physics-Informed Identification of Dynamic Systems
Impulse responses are cast as random atomic superpositions of stable poles inside a disk and recovered through constrained convex optimization that encodes engineering priors.
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Sobolev Regularized MMD Gradient Flow
Sobolev regularization on the witness function enables global convergence of MMD gradient flows for both sampling and generative modeling without isoperimetric assumptions.
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UD-DML: Uniform Design Subsampling for Double Machine Learning over Massive Data
UD-DML creates balanced representative subsamples via uniform design in PCA space for efficient double machine learning estimation of average treatment effects on large datasets.