Derives explicit approximation and generalization rates for multi-input neural operators in Sobolev spaces that quantify each input's contribution to the error.
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Nudging with learned surrogate dynamics converges exponentially to an explicit error floor determined by surrogate error and observation noise, with training data requirements quantified for noise-free cases.
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Continuous Data Assimilation with Learned Surrogate Dynamics
Nudging with learned surrogate dynamics converges exponentially to an explicit error floor determined by surrogate error and observation noise, with training data requirements quantified for noise-free cases.