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I am interested in Machine learning in general and finding the missing link between solely data-driven modeling and physics-based modeling in particular. This involves nonlinear embeddings for dynamical systems identification and discovery, time-dependent surrogate modeling, and reduced order modeling for execution-intensive analyses such as uncertainty quantification, sensitivity analysis, and data assimilation applied to multi-scale real-world complex problems.
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