Author

Melika Baghi

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Preprint Oct 2026

A Product-Grassmann Tucker Surrogate for Parametric Field Prediction

Many-query applications such as design optimization, uncertainty quantification and digital twins call for repeated full-field predictions from expensive partial differential equation (PDE) simulations. Grassmannian reduced-order models interpolate parameter-dependent subspaces but commonly vectorize the spatial field,...

Melika Baghi, Xiao Liu, K. Paynabar · 0 citations

Conference

Enhancing Reduced-Order Models in High-Dimensional Engineering Applications through Gradient Boosting Tree

This research explores a novel integration of XGBoost, known for its predictive accuracy and capability to handle complex relationships, with an innovative mapping technique between a multi-dimensional Euclidean space and the Grassmann Manifold, which represents low-dimensional subspaces, making XGBoost suitable with m...

Melika Baghi, Xiao Liu, K. Paynabar · IISE Annual Conference &... · 0 citations

#machine learning Preprint Sep 2026

Pooling and Drift in Delayed Bandits

This work proves that on generated data, the state channel cuts regret by up to 79 percent against action-level weighting and, on the funnel family, by 32 to 68 percent against a tuned minimax-optimal method.

Melika Baghi · 0 citations