Deep learning foundation model framework for the surrogate modeling for efficient design exploration

The increasing complexity and computational demands of design exploration in engineering and architecture necessitate innovative approaches to streamline the process. This paper presents a novel Deep Learning Foundation Model Framework for surrogate modeling, aimed at enhancing the efficiency of design exploration.

Our framework leverages advanced deep learning techniques to create highly accurate surrogate models that approximate the performance metrics of complex design simulations with significantly reduced computational cost. By integrating these surrogate models into the design workflow, designers can rapidly evaluate numerous design alternatives and identify optimal solutions without the need for extensive computational resources.

The framework is validated through a series of case studies in architectural design, demonstrating its capability to maintain high fidelity in performance predictions while drastically reducing simulation time. This research not only advances the application of deep learning in design exploration but also provides a scalable solution for tackling the challenges of high-dimensional, multi-objective design problems. The implications of this framework extend to various domains where efficient design iteration and performance optimization are critical.