In the chemical sciences, many laboratory experiments, environmental and industrial processes, as well as modeling exercises, are characterized by large numbers of input variables. A general objective in such cases is an exploration of the high-dimensional input variable space as thoroughly as possible for its impact on observable system behavior, often with either optimization in mind or simply for achieving a better understanding of the phenomena involved. An important concern when undertaking these explorations is the number of experiments or modeling excursions necessary to effectively learn the system input --> output behavior, which is typically a nonlinear relationship. Although simple logic suggests that the number of runs could grow exponentially with the number of input variables, broadscale evidence indicates that the required effort often scales far more comfortably. This paper considers an emerging family of high dimensional model representation concepts and techniques capable of dealing with such input --> output problems in a practical fashion. A summary of the state of the subject is presented. along with several illustrations from various areas in the chemical sciences.