Very Short Literature Survey From Supervised Learning To Surrogate Modeling
It provides a literature survey for those familiar with supervised learning, but it is incremental as it primarily reviews and synthesizes existing concepts without presenting new methods or results.
This paper introduces surrogate modeling as a new branch of supervised learning to address the challenges of modeling highly complex systems in an era of abundant computational resources, focusing on its necessity, challenges, and visions.
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.