kEDMD for stochastic systems has L^∞ error bounds that separate a deterministic fill-distance term from a probabilistic Monte Carlo sampling term.
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2 Pith papers cite this work. Polarity classification is still indexing.
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math.DS 2years
2025 2verdicts
UNVERDICTED 2representative citing papers
Applies Koopman operator with EDMD and tailored dictionary to an SIRSD model to identify dominant modes and predict outbreak peaks from synthetic data across four diseases.
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Koopman for stochastic dynamics: error bounds for kernel extended dynamic mode decomposition
kEDMD for stochastic systems has L^∞ error bounds that separate a deterministic fill-distance term from a probabilistic Monte Carlo sampling term.
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A Koopman Operator Framework for Nonlinear Epidemic Dynamics: Application to an SIRSD Model
Applies Koopman operator with EDMD and tailored dictionary to an SIRSD model to identify dominant modes and predict outbreak peaks from synthetic data across four diseases.