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Modeling Simulation Science

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Modeling Simulation Science

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Research Frontiers in Reduced Order Modeling Techniques

Development of low-dimensional approximations of high-dimensional systems to enable efficient computation while maintaining accuracy.

Nonlinear Manifold Learning in High-Dimensional Dynamics
Physics-Informed Neural Operators for Real-Time Prediction
Parametric Uncertainty Propagation in Reduced Systems
Adaptive Basis Construction for Transient Phenomena
Data-Driven Model Reduction Across Coupled Physics
Singular Perturbation Methods in Multiscale Reduction
Projection-Free Surrogate Modeling for Complex Geometries
Stability-Preserving Reduction of Nonlinear Control Systems
Spectral Methods for Reduced-Order Bifurcation Analysis
Cognitive Dimensionality Reduction in Heterogeneous Microstructures

All Modeling & Simulation Science PhD categories