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Computational Mathematics

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Computational Mathematics

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Research Frontiers in High-Dimensional Uncertainty Quantification and Sampling

Advanced techniques for propagating uncertainty through complex mathematical models using sparse grids and quasi-Monte Carlo methods.

Curse-Breaking: Adaptive Sampling in Exponential Dimensions
Rare Event Capture Beyond Classical Importance Weighting
Manifold Learning for High-Dimensional Probability Landscapes
Derivative-Free Uncertainty in Black-Box Parameter Spaces
Coupling Strategies Across Correlated High-Dimensional Posteriors
Multilevel Methods for Stratified Uncertainty Quantification
Non-Intrusive Surrogate Models Under Dimension Growth
Transport Maps in Ultra-High-Dimensional Sampling
Active Learning Frontiers in Sequential Uncertainty Reduction
Information Geometry of Multimodal Posterior Exploration

All Computational Mathematics PhD categories