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Modeling Simulation Science

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Modeling Simulation Science

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Research Frontiers in Uncertainty Quantification Methods

Rigorous mathematical approaches for characterizing, propagating, and managing uncertainties in computational models and simulation predictions.

Epistemic-Aleatoric Uncertainty Separation in Surrogate Models
Bayesian Inverse Problems with Intractable Likelihoods
Rare Event Estimation Beyond Traditional Importance Sampling
Multi-Fidelity Uncertainty Fusion in Coupled Physics
Active Learning for High-Dimensional Uncertainty Spaces
Polynomial Chaos Expansion in Extreme-Scale Computing
Sensitivity Analysis Without Gradient Information
Propagating Model Discrepancy Through Complex Systems
Machine Learning Surrogates with Certified Uncertainty Bounds
Adaptive Sampling Strategies for Non-Stationary Parametric Uncertainty

All Modeling & Simulation Science PhD categories