## 4 Resilience and Sustainability of the AI-Based DTs

Table 5: _Cont._to minimize resource consumption, ensure the ethical use of AI, and maintain long-term operational effectiveness [50; 51; 52; 53]. Studies have shown how the level of decomposition, model size, and submodel structure affect the accuracy, efficiency, and adaptivity of DTs [54; 55]. Research has shown that the level of decomposition in DT models--the way a complex system is broken down into smaller components--directly affects the granularity and accuracy of simulations. Finer decomposition can lead to more accurate modeling of individual parts, but it can also increase computational load and complexity. Similarly, the overall model size affects performance: larger models can represent systems more comprehensively but require more processing power and memory, which can limit real-time responsiveness. The structure and interconnectivity of submodels also influence adaptability, determining how easily the DT can be updated or reconfigured when system parameters change. Therefore, finding the right balance between decomposition, model size, and submodel architecture is crucial to optimizing the performance and scalability of DT applications. Studies indicate that excessive decomposition can lead to computational inefficiencies, while insufficient decomposition can result in oversimplified models that lack precision. The size of models and submodels directly affects simulation speed and real-time responsiveness, requiring optimization techniques to balance detail and efficiency [56; 57]. The structural organization of submodels affects interoperability, and poorly structured submodels can create bottlenecks in digital twin performance [58; 59]. Despite progress, there is still a need for fast-response models that can dynamically adapt to changing conditions while maintaining computational efficiency [60; 61; 62; 63; 64]. Adaptive personalized DTs require modular architectures that facilitate the seamless integration of AI-based algorithms and domain-specific models [65; 66]. Recent research has focused on developing hybrid modeling approaches that combine data-driven AI techniques with domain knowledge to enhance adaptivity. Improving performance depends on efficient data processing pipelines that support real-time decision making while minimizing computational overhead [67; 68; 69]. Integrating multiple algorithms into DTs requires a robust framework that ensures consistency, synchronization, and error management; hence, future research should emphasize scalable and interoperable designs that enable the rapid deployment of personalized DTs across domains [70]. AI-based DTs with partial domain adaptation are advanced virtual replicas of physical systems that leverage AI to improve real-world performance while selectively adapting to new environments [71; 72]. AI-based DTs with partial domain adaptation can transfer knowledge from one environment to another, reducing the need for extensive retraining and accelerating implementation in new environments. This adaptability allows DTs to remain effective even when operational conditions or data distribution change, for example, when transferring from one urban infrastructure to another or between different hospital systems. By improving modeling efficiency and response time, DTs support real-time optimization and resource management across various sectors. For example, in autonomous systems, they help vehicles adapt to new conditions with minimal manual intervention, increasing operational reliability. Furthermore, by enabling smarter resource utilization and predictive analytics, they contribute to more sustainable practices in energy consumption, waste reduction, and long-term system planning. These models integrate real-time sensor data and machine learning to dynamically simulate, predict, and optimize operations. Partial domain adaptation ensures that the DT learns only the relevant changes from the new domain without retraining the entire model, reducing computational costs [73; 74].