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