17 citations · 17 across the 2 of their papers we have counts for
2 papers
cs.LG2026
Trajectory-Optimized Time Reparameterization for Learning-Compatible Reduced-Order Modeling of Stiff Dynamical Systems
Joe Standridge, Daniel Livescu, Paul Cizmas
Stiff dynamical systems present a challenge for machine-learning reduced-order models (ML-ROMs), as explicit time integration becomes unstable in stiff regimes while implicit integ…
physics.comp-ph2020★ 17 cited
An Efficient Proper Orthogonal Decomposition based Reduced-Order Model
Elizabeth H. Krath, Forrest L. Carpenter, Paul G. A. Cizmas +1
This paper presents a novel, more efficient proper orthogonal decomposition (POD) based reduced-order model (ROM) for compressible flows. In this POD model the governing equations,…