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reduction, with an emphasis on maintaining physical consistency, numerical stability, and real-time data assimilation within reduced-order models. Primary application areas include computational physics and
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at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model reduction, with
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