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Learn how to revolutionize structural health monitoring through physics-informed digital twins in this 21-minute conference talk from the Data Science Conference MENA 25. Discover how to overcome traditional SHM challenges including data scarcity, computational inefficiency, and inability to predict rare failure scenarios by integrating digital twins with advanced technologies like physics-informed machine learning, generative AI, and dynamic diffusion models for real-time damage identification and predictive maintenance. Explore how generative AI synthesizes realistic failure data to develop robust models when training data is limited, while physics-informed machine learning combines domain-specific knowledge with data-driven approaches for precise and interpretable predictions. Master dynamic diffusion models for probabilistic anomaly detection that effectively handle noisy, real-world sensor data. Examine the integration of self-healing materials within digital twin frameworks to create resilient, adaptive structures capable of autonomous repair. Gain practical insights through case studies using MATLAB, ANSYS, and Python for implementing these methodologies in both research and industrial applications. Understand how next-generation digital twins can transform structural health monitoring and damage detection for smarter, more resilient infrastructure development, with actionable knowledge applicable to civil, mechanical, and aerospace engineering projects.
