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Applied Mathematics

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Applied Mathematics

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Research Frontiers in Uncertainty Quantification in Simulations

Statistical frameworks for propagating, analyzing, and reducing uncertainty in computational models and scientific simulations.

Rare Event Prediction in High-Dimensional Stochastic Systems
Surrogate Models and Bayesian Inverse Problems
Polynomial Chaos Expansion Beyond Classical Assumptions
Data Assimilation in Non-Linear Chaotic Dynamics
Multi-Fidelity Uncertainty Propagation in Complex Physics
Sensitivity Analysis at Extreme Parameter Regimes
Machine Learning Surrogates with Quantified Confidence Bounds
Uncertainty in Digital Twin Evolution and Prediction
Extremal Dependence Structures in Coupled Simulations
Information-Theoretic Approaches to Model Discrepancy

All Applied Mathematics PhD categories