We derive universal upper estimates for model prediction error under moderate but otherwise unknown model uncertainty. Our estimates give upper bounds on the leading-order trajectory uncertainty arising along model trajectories, solely as functions of the invariants of the known Cauchy–Green strain tensor of the model. Our bounds turn out to be optimal, which means that they cannot be improved for general systems. The quantity relating the leading-order trajectory-uncertainty to the model uncertainty is the model sensitivity (MS), which we find to be a useful tool for a quick global assessment of the impact of modeling uncertainties in various domains of the phase space. By examining the expectation that finite-time Lyapunov exponents capture sensitivity to modeling errors, we show that this does not generally follow. However, we find that certain important features of the finite-time Lyapunov exponent persist in the MS field.
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Research Article| November 24 2020
Universal upper estimate for prediction errors under moderate model uncertainty
Bálint Kaszás ;
Bálint Kaszás, George Haller; Universal upper estimate for prediction errors under moderate model uncertainty. Chaos 1 November 2020; 30 (11): 113144. https://doi.org/10.1063/5.0021665
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