  The past century was era of linear systems. Either systems (especially
industrial ones) were simple (quasi)linear or linear approximations were
accurate enough. In addition, just at the ending decades of the century
profusion of computing devices were available, before then due to lack of
computational resources it was not easy to evaluate available nonlinear system
studies. At the moment both these two conditions changed, systems are highly
complex and also pervasive amount of computation strength is cheap and easy to
achieve. For recent era, a new branch of supervised learning well known as
surrogate modeling (meta-modeling, surface modeling) has been devised which
aimed at answering new needs of modeling realm. This short literature survey is
on to introduce surrogate modeling to whom is familiar with the concepts of
supervised learning. Necessity, challenges and visions of the topic are
considered.
