Multifidelity Sampling for High-Dimensional Spatial Uncertainty Modeling: Application to storm surge

Music City Center 

We introduce a transport map approach for spatial uncertainty quantification in multifidelity problems. By learning invertible transformations between target distribution and reference distribution, the method enables efficient sampling from complex high-fidelity distributions. It captures nonlinear, non-Gaussian dependencies without assuming restrictive functional forms, and scales to high-dimensional spatial settings. Exploiting spatial locality and adaptive parameterization, it achieves accurate inference at reduced cost. Applied to coastal flooding, the method more effectively represents storm surge variability than standard Gaussian process models, demonstrating the strength of transport-based solutions for multifidelity inference in environmental systems.

Bayesian inference

EM algorithm

Transport Maps 

Section on Bayesian Statistical Science