Updating surrogate models in early building design via tabular transfer learning

Abstract

Surrogate models provide estimations of design performance objectives, which can yield rapid feedback in early building design. However, constructing parametric models and generating data to train surrogate models is time-consuming. Once established, designers are discouraged from modifying the structure of the design space, which may require additional simulation and retraining. To address this limitation, we propose a new workflow that incorporates a tabular transfer learning approach, coupled with a random walks sampling technique. This approach preserves the knowledge from the initial dataset, thus reducing the number of simulations required to update the surrogate model when new variables are introduced. Through a façade design space case study with a daylighting performance objective, we demonstrate that fewer samples are needed to update the surrogate model and achieve adequate model performance compared to classical interpretable classifiers when only a few new samples are possible or desired. As the number of new samples increases, the performances of some classical methods using traditional sampling improve to eventually meet or surpass the tabular transfer learning method.