This book demonstrates how to describe and analyze a system's behavior
and extract the desired prediction and control algorithms from this
analysis. A typical prediction is based on observing similar situations
in the past, knowing the outcomes of these past situations, and
expecting that the future outcome of the current situation will be
similar to these past observed outcomes. In mathematical terms,
similarity corresponds to symmetry, and similarity of outcomes to
invariance.
This book shows how symmetries can be used in all classes of algorithmic
problems of sciences and engineering: from analysis to prediction to
control. Applications cover chemistry, geosciences, intelligent control,
neural networks, quantum physics, and thermal physics. Specifically, it
is shown how the approach based on symmetry and similarity can be used
in the analysis of real-life systems, in the algorithms of prediction,
and in the algorithms of control.