Hybrid Digital Twins (HDTwins) offer a novel approach to modeling dynamical systems by combining mechanistic and neural components, effectively leveraging domain knowledge while enhancing flexibility. However, existing hybrid models typically rely on manually defined architectures, limiting adaptability and generalization—particularly in data-scarce or unseen scenarios. To address this, we introduce HDTwinGen, an evolutionary algorithm that utilizes Large Language Models (LLMs) to autonomously generate, optimize, and refine hybrid digital twin architectures. Through iterative LLM-driven proposals and parameter optimization, HDTwinGen explores a vast design space, enabling the evolution of increasingly robust and generalizable HDTwins. Empirical results show that HDTwinGen surpasses conventional methods, yielding models that are not only sample-efficient but also adept at adapting to novel conditions, advancing the state of Digital Twin technology in dynamic real-world applications.
