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models offer a powerful means to understand stroke mechanisms, predict treatment outcomes, and personalize patient care. By integrating numerical techniques like the finite element method and machine
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will be augmented with atomistic structure data from electronic structure theory and STEM image simulations. All data will be combined into an automated workflow that predicts thermodynamically stable
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decision making to better allocate resources, and can support whole of lifecycle infrastructure asset management. User-centric infrastructure can be delivered to proactively identify and predict maintenance
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for a Condition-based maintenance (CBM) which holds the promise of predicting machinery maintenance requirements based on process performance measurements. Diagnostics and Prognostics are essential