## 1 Introduction
It enables realistic, yet human-operator-level perceptual simulations of complex industrial systems by generating synthetic data and scenarios, helping to predict and diagnose faults with greater accuracy. This increases the ability of the operator--cyber-physical system to detect trends, upcoming anomalies, identify their root causes and predict failures before they occur, compared to solutions without GenAI [16]. GenAI also helps improve the quality and quantity of training data by addressing limitations in sensor data availability and improving the robustness of the ML model [17]. Additionally, GenAI supports real-time updates to DTs by synthesizing data from different sources, ensuring that the twin reflects the current state of the asset. It facilitates the analysis of alternative \"what if\" scenarios by generating multiple potential failure scenarios (instead of the most likely ones, as before), enabling proactive maintenance strategies [18]. The technology improves decision-making by providing actionable insights derived from patterns and correlations identified in the generated data. GenAI also helps optimize system performance by simulating the impact of different maintenance actions, allowing operators to select the most effective intervention [19]. As Industry 5.0 emphasizes human-centric and sustainable solutions, GenAI in DTs aligns with these goals, enabling smarter, more efficient, and less resource-intensive maintenance practices [20]. GenAI is a rapidly growing area of research in deep learning. GenAI algorithms generate new realistic data in various modalities (text, images, music, three-dimensional (3D) models) and multimodalities [21]. Key features of GenAI include the ability to synthesize data and learning distributions. GenAI systems can synthesize new data that has features from other elements of the data set [22]. Thus, a GenAI model trained on images of dogs can create new, realistic images of dogs that do not exist in the training set [23]. GenAI models probability distributions of data, from which it can sample new instances that follow learned patterns [24]. GenAI uses state-of-the-art deep learning architectures that are rapidly evolving. The most important of them are the following:  
* Generative Adversarial Networks (GANs)--involve the interaction between two neural networks: a generator and a discriminator. The generator creates synthetic data, while the discriminator distinguishes real from fake data. Through iterative competition, GANs generate highly realistic results and are used for image synthesis (DeepArt, DeepFake), video content generation, and style transfer (painting, photography);
* Variational Autoencoders (VAE)--encode input data into a latent representation and decode it back, following a predefined distribution. This enables smooth interpolation between data points for image reconstruction, anomaly detection, or drug discovery;
* Transformer models--use self-attention mechanisms to generate coherent and contextually relevant sequences within language models (ChatGPT, BERT) or description-based image synthesis (DALL-E);
* Diffusion models--an alternative to GANs, learn to reverse the noise process applied to data, gradually producing realistic results in high-quality image synthesis or molecular structure generation [25; 26; 27]. GenAI allows us to create content that integrates multiple modalities, e.g., generating an image from a text description. It offers computationally efficient models that scale to large data sets. For the above reasons, GenAI in a sense expands creativity, providing a selection from a larger set of quickly generated data with similar characteristics [28]. This allows us to generate synthetic data (industrial, medical, etc.) for training AI models or DTs, including without concerns about privacy or security, creating unique narratives and visualizations, accelerating discoveries by generating molecular structures, optimizing engineering designs and simulating physical phenomena [29].