As digital twins emerge as influential tools for real-time prediction, optimization, monitoring, and decision-making, StrömungsRaum®, developed by IANUS Simulation, represents a pioneering digital twin ecosystem that pushes the boundaries of computational sciences and AI. This presentation will delve into StrömungsRaum's unique capabilities, including its advanced algorithms, user-friendly interfaces, and cutting-edge technologies, all of which contribute to a custom solution for various application areas. Central to our discussion is the mutability of the digital twin's geometrical layer, a feature that allows for flexible adaptation and fine-tuning of simulation models. This mutability, when paired with StrömungsRaum's hyperautomation capabilities in geometry processing, opens up new avenues for real-time modifications, significantly enhancing the responsiveness and adaptability of digital twins. A key component of our exploration is the innovative use of synthetic data in combination with high-performance computing [1]. Utilizing StrömungsRaum's ability to handle maximum data throughput, we will elucidate how synthetic data, when effectively processed and analyzed, can be instrumental in generating highly accurate models for manufacturing. Our discussion will further delve into the creation and refinement of these generative models, examining how they can be used to improve prediction accuracies and streamline manufacturing processes. We will showcase specific use cases such as the optimization of profile extrusion tools and hot runner systems, emphasizing the synergy of AI technologies, synthetic data, and simulation data in improving sensor-based control strategies, which leads to more efficient processes, higher product quality, and reduced costs. Additionally, we will explore the significance of StrömungsRaum's cloud-based nature in fostering scalability and security, enabling flexible and efficient problem-solving on a diverse range of computational platforms [2][3]. The presentation will conclude with a look into the future of digital twin technologies, discussing how platforms like StrömungsRaum are poised to play a crucial role in addressing societal and economic challenges through the combined power of computational sciences and AI. REFERENCES H. Ruelmann, M. Geveler, D. Ribbrock, S. Turek, Basic Machine Learning Approaches for the acceleration of PDE Simulations and Realization in the FEAT3 Software. Vermolen, F., Vuik, C., Lect