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Learn to revolutionize structural health monitoring through this comprehensive conference talk that introduces physics-driven digital twins for real-time infrastructure assessment. Discover how to overcome traditional SHM challenges including data scarcity, computational inefficiency, and unpredictable failure scenarios by integrating digital twins with cutting-edge technologies such as physics-informed machine learning, generative AI, and dynamic diffusion models. Explore how generative AI synthesizes realistic failure data to develop robust models when real-world data is limited, while physics-informed machine learning combines domain expertise with data-driven approaches for precise and interpretable predictions. Master dynamic diffusion models for probabilistic anomaly detection that effectively handle noisy sensor data from real-world environments. Examine the integration of self-healing materials within digital twin frameworks to create adaptive structures capable of autonomous repair and enhanced resilience. Gain practical experience through case studies utilizing industry-standard tools including MATLAB, ANSYS, and Python for implementing these advanced methodologies in both research and industrial settings. Understand how next-generation digital twins enable real-time damage identification and predictive maintenance with remarkable accuracy, transforming traditional approaches to infrastructure monitoring. Acquire actionable insights for tackling current engineering challenges and contributing to the development of smarter, more resilient infrastructure systems across civil, mechanical, and aerospace engineering applications.
