A note on statistical consistency of numerical integrators for multi-scale dynamics

A minimal requirement for simulating multi-scale systems is to reproduce the statistical behavior of the slow variables. In particular, a good numerical method should accurately aproximate the probability density function of the continuous-time slow variables. In this note we use results from homoge...

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Bibliographic Details
Published inarXiv.org
Main Authors Frank, J, Gottwald, G A
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 11.04.2018
Subjects
Bias
Computer simulation
Continuity (mathematics)
Economic models
Error analysis
Error correction
Fields (mathematics)
Homogenization
Integrators
Mathematical models
Numerical methods
Probability density functions
Solvers
Statistical analysis
Trajectories
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Summary:A minimal requirement for simulating multi-scale systems is to reproduce the statistical behavior of the slow variables. In particular, a good numerical method should accurately aproximate the probability density function of the continuous-time slow variables. In this note we use results from homogenization and from backward error analysis to quantify how errors of time integrators affect the mean behavior of trajectories. We show that numerical simulations converge, not to the exact probability density function (pdf) of the homogenized multi-scale system, but rather to that of the homogenized modified equations following from backward error analysis. Using homogenization theory we find that the observed statistical bias is exacerbated for multi-scale systems driven by fast chaotic dynamics that decorrelate insufficiently rapidly. This suggests that to resolve the statistical behavior of trajectories in certain multi-scale systems solvers of sufficiently high order are necessary. Alternatively, backward error analysis suggests the form of an amended vector field that corrects the lowest order bias in Euler's method. The resulting scheme, a second order Taylor method, avoids any statistical drift bias. We corroborate our analysis with a numerical example.
ISSN:2331-8422