108 citations · 525 across the 62 of their papers we have counts for
5 papers · 1 filter
A Two-Level Galerkin Reduced Order Model for the Steady Navier-Stokes Equations
Dylan Park, Changhong Mou, Honghu Liu +2
We propose, analyze, and investigate numerically a novel two-level Galerkin reduced order model (2L-ROM) for the efficient and accurate numerical simulation of the steady Navier-St…
The Model Forest Ensemble Kalman Filter
Andrey A Popov, Adrian Sandu
Traditional data assimilation uses information obtained from the propagation of one physics-driven model and combines it with information derived from real-world observations in or…
A Meta-learning Formulation of the Autoencoder Problem for Non-linear Dimensionality Reduction
Andrey A. Popov, Arash Sarshar, Austin Chennault +1
A rapidly growing area of research is the use of machine learning approaches such as autoencoders for dimensionality reduction of data and models in scientific applications. We sho…
Physics-informed neural networks for PDE-constrained optimization and control
Jostein Barry-Straume, Arash Sarshar, Andrey A. Popov +1
A fundamental problem in science and engineering is designing optimal control policies that steer a given system towards a desired outcome. This work proposes Control Physics-Infor…
Eliminating Order Reduction on Linear, Time-Dependent ODEs with GARK Methods
Steven Roberts, Adrian Sandu
When applied to stiff, linear differential equations with time-dependent forcing, Runge-Kutta methods can exhibit convergence rates lower than predicted by the classical order cond…