Digital Twins and AI

Digital twins and artificial intelligence are fundamentally changing the way we design, operate, and optimize fluid systems - ranging from water distribution and pumps to reactors and wind turbines. Where traditional practice relied heavily on steady-state assumptions, empirical correlations, and offline testing, we’re now empowered by real-time models and data-driven insights that enable us to predict behaviour, diagnose issues, and continuously improve performance. This seminar delves into the integration of Digital Twin and AI technologies in fluid mechanics, with a special focus on physics-based modelling, data assimilation, and machine learning for monitoring, control, and decision support. We will explore hybrid approaches that combine first-principles models with AI like reduced-order models, surrogate modelling, and physics-informed learning and discuss practical deployment in industrial settings. Industry and academic experts will share methods, tools, and case studies on creating and operating fluid-mechanical digital twins, applying AI for soft sensing and anomaly detection, and using hybrid models for real-time optimization and control. Presentations will demonstrate how these technologies are helping to reduce time-to-decision, boost reliability and safety, improve energy efficiency, and enable more resilient, data-driven fluid systems.