Recent work in scientific machine learning (SciML) has focused on incorporating partial
differential equation (PDE) information into the learning process. Much of this work has …

In this article, we introduce a methodology for improving the accuracy and efficiency of
reduced order models (ROMs) constructed using the least‐squares Petrov–Galerkin (LSPG) …

Although projection-based reduced-order models (PROMs) have existed for decades, they
have rarely been applied to large, nonlinear, multiscale, and multi-physics systems due to …

View Video Presentation: https://doi. org/10.2514/6.2021-2716. vid We present a projection-
based model reduction method for efficient solution of computational fluid dynamics …

Ocean modeling, in a climate-modeling context, requires long time-horizons over global
scales, which when combined with accurate resolution in time and space makes simulations …

This article provides a reduced-order modelling framework for turbulent compressible flows
discretized by the use of finite volume approaches. The basic idea behind this work is the …

A local adaptive Reduced Basis approach is presented with a focus on the accuracy in
reconstructing unsteady vortex-dominated flow fields generated by impulsively started lifting …

To draw rigorous conclusions from scientific data, Bayesian statistics requires
computationally efficient methods for posterior inference as well as models that are both …

This thesis investigates the development and application of projection-based reduced-order
models (PROMs) to mitigate the exorbitant computational cost of high-fidelity numerical …

This report summarizes fiscal year 2018 (FY18) progress towards developing, implementing
(within the SPARC finite volume flow solver) and evaluating the viability of projection-based …