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A new strategy, namely the "clean numerical simulation" (CNS), was proposed(J. Computational Physics, 418:109629, 2020) to gain reliable/convergentsimulations (with negligible numerical noises) of spatiotemporal chaoticsystems in a long enough interval of time, which provide us benchmark solutionfor comparison. Here we illustrate that machine learning (ML) can always givegood enough fitting predictions of a spatiotemporal chaos by using, separately,two quite different training sets: one is the "clean database" given by the CNSwith negligible numerical noises, the other is the "polluted database" given bythe traditional algorithms in single/double precision with considerably largenumerical noises. However, even in statistics, the ML predictions based on the"polluted database" are quite different from those based on the "cleandatabase". It illustrates that the database noises have huge influences on MLpredictions of some spatiotemporal chaos, even in statistics. Thus, we must usea "clean" database for machine learning of some spatiotemporal chaos. Thissurprising result might open a new door and possibility to study machinelearning.

Influence of database noises to machine learning for spatiotemporal chaos