A Survey on Digital Twin–Assisted Reconfigurable Intelligent Surfaces: Fundamentals, Challenges, and Applications

Reconfigurable intelligent surfaces (RISs) have emerged as a compelling technology for next‐generation wireless networks, enabling programmable control of electromagnetic wave propagation to improve coverage, spectral efficiency, and energy efficiency. However, practical deployment remains constrained by challenges in channel estimation, real‐time phase‐shift optimization, and scalability in dynamic environments. Digital twin (DT) technology provides a promising solution by constructing high‐fidelity virtual replicas of wireless systems that enable predictive modeling, continuous monitoring, and closed‐loop RIS control. While recent works have begun exploring DT–RIS integration, existing surveys lack a unified framework that connects application‐level objectives with the underlying computational mechanisms. To address this gap, this paper introduces a dual‐perspective taxonomy that organizes DT–RIS research along two complementary dimensions: (i) functional enablers, covering latency reduction and MEC performance, resource management, and security; and (ii) methodological enablers, including reinforcement learning, federated learning, generative models, and convex optimization. This joint perspective reveals how specific algorithmic strategies underpin distinct network objectives, exposing interdependencies overlooked in prior single‐axis analyses. Building on this framework, the survey synthesizes DT–RIS applications and identifies a fundamental trade‐off between DT modeling fidelity and synchronization overhead that limits real‐time operation. Finally, the paper outlines key open challenges, including CSI acquisition overhead, DT update latency, and control signaling cost, and discusses directions toward scalable and practically deployable DT–RIS architectures for 6G networks.