## 4 Resilience and Sustainability of the AI-Based DTs  
The resilience of AI-based DTs is a critical challenge due to their reliance on data quality, real-time processing, and adaptation to changing environments. Hybrid DTs, which combine physics-based models with AI-based simulations, face challenges in maintaining consistency and robustness, as discrepancies between model types can lead to inaccurate predictions [38,39]. Multi-level DTs, which integrate multiple hierarchical model layers (e.g., component, system, and enterprise level), struggle with synchronization and consistency across levels, which impacts their resilience [40,41]. Reused DTs, which are adapted from existing models for new applications, can inherit outdated assumptions and biases, reducing their effectiveness in dynamic environments [42, 43, 44]. AI-based DTs are dependent on massive amounts of sensor data, making them vulnerable to cyberattacks, sensor failures, and hostile AI threats that weaken their resilience. The constant evolution of AI models requires constant updates, which can introduce new errors or lead to model drift, which complicates the long-term stability of DTs [45,46]. Hybrid DTs can have explainability issues because AI-generated insights can conflict with deterministic physics-based models, reducing trust in decision-making processes. Multi-level DTs require scalable data integration, and failures at one level can cascade through the system, leading to instability in the connected processes. Reused DTs often struggle to adapt to new operational contexts due to differences in physical conditions, data sources, or regulatory constraints [47, 48, 49]. Ensuring the stability of AI-based DTs requires balancing computational efficiency with the need for high-fidelity simulations, which can be resource-intensive. AI-based predictions in DTs can deteriorate over time due to changing real-world conditions, requiring robust retraining mechanisms to ensure long-term resilience. Hybrid DTs can suffer from model divergence if AI components evolve independently of physics-based rules, leading to inconsistencies. Multi-level DTs must address the challenges of interoperability between different AI models and simulation frameworks to remain resilient across domains. Reused DTs need adaptive learning and self-correction mechanisms to ensure they remain relevant and accurate in new contexts. The resilience of AI-based DTs depends on their ability \begin{table}
\begin{tabular}{c c} \hline
**Way of Development** & **Details** \\ \hline Predictive analytics & Advanced DTs use predictive analytics to anticipate potential issues, enabling proactive maintenance and decision making \\ \hline AI-driven optimization & AI-driven DTs leverage deep learning to dynamically optimize processes, continually improving their behavior based on incoming data \\ \hline Generative AI for scenario simulation & AI-driven DTs, including generative AI models, create synthetic scenarios to test and optimize responses before applying them to the real world \\ \hline Autonomous decision making & As complexity increases, DTs are moving toward AI-driven autonomy, making data-driven decisions with minimal human intervention \\ \hline Behavioral and contextual adaptation & AI-based DTs can analyze user behavior and environmental contexts, adapting their capabilities to specific needs \\ \hline Integration of natural language & Generative AI empowers DTs by enabling human-like conversational interactions, improving usability and adaptability \\ \hline Multi-agent AI systems & Advanced AI-based twins feature multiple AI agents that work together to optimize adaptation and decision making across the system \\ \hline Self-learning and evolution & The most complex DTs leverage reinforcement learning, enabling continuous self-improvement without predefined rules \\ \hline Human–AI collaboration & Ultimately, DTs evolve into AI-assisted systems where human and AI experts work together to provide optimal adaptation and innovation \\ \hline \end{tabular}
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