16.12
Physical Models to Generate Additional Data
When there is no theoretical understanding of the target
function, training from examples is one of few options. In many cases, however,
there may be a physical model that can provide useful information even if it is
not completely accurate. Possibilities include
-
a rough model exists that accounts for the main variables
only and ignores small details;
-
an accurate model exists, but is too cumbersome to use in
practice; or
-
an exact model exists, but it is difficult or expensive to
measure all the variables needed by the model.
Models can be useful to generate artificial training data for
cases where it is difficult to obtain real training data. In physical control
systems, for example, it may not be practical to obtain data for unusual
operating modes such as process faults. Use of a model to generate additional
artificial data for unusual operating modes of a steel rolling mill is described
by Röscheisen, Hofmann, and Tresp [323].