WITHDRAWN Fast global-local reduced order models using neural Galerkin projections

Music City Center 

Computationally expensive numerical solutions of partial difference equation models (PDE)s are critical in understanding complex real-world phenomena. However, the utility of these numeric simulations in many applications is limited by the computational cost of running these models over a breadth of initial conditions necessary to characterize the space of feasible solutions. We propose a novel surrogate framework RANDPROM that combines cutting-edge reduced-order approximations of PDEs within a Bayesian hierarchical model that enables accurate and precise predictions of the PDE solutions at initial conditions which are not included in training data. Using simulations of tsunami wave height as an example dataset, we demonstrate the potential for RANDPROM to produce near real-time predictions of wave height which demonstrates the potential of the RANDPROM surrogate framework to contribute to real-world decision making.

PDE surrogates

Hierarchical models

Uncertainty quantification 

Section on Statistics in Defense and National Security