A parametric design integrated sampling and general training approach for optimal control oriented surrogate models of light-related quantities

This study presents a general method for determining optimal control oriented surrogate models of light-related quantities. It is termed the incidence operator method comprising of sampling, model training and model export. In contrast to matrix-based methods, machine learning constitutes a fundamental component of this new method. This entails the ability to represent complex dependencies of light-related quantities on adjustable material and geometric properties, as well as the possibility to export models using a standardized format (e.g. FMI, ONNX). Furthermore, components were developed to streamline the sampling of training data from parametric designs. The associated higher resolution reveals spatial discontinuities, for which a novel modeling approach and integration methodology have been developed. The incidence operator method was validated with the enhanced two-phase method using two scenes. Based on annual simulations, the simple scene demonstrates high agreement ((Formula presented.)), while the complex fenestration scene exhibits spatial discontinuities resulting in an increased deviation ((Formula presented.)).