17 citations · 30 across the 10 of their papers we have counts for
4 papers · 1 filter
Reduced Basis Approximations of Parameterized Dynamical Partial Differential Equations via Neural Networks
Peter Sentz, Kristian Beckwith, Eric C. Cyr +2
Projection-based reduced order models are effective at approximating parameter-dependent differential equations that are parametrically separable. When parametric separability is n…
A Monolithic Algebraic Multigrid Framework for Multiphysics Applications with Examples from Resistive MHD
Peter Ohm, Tobias Wiesner, Eric C. Cyr +3
A multigrid framework is described for multiphysics applications. The framework allows one to construct, adapt, and tailor a monolithic multigrid methodology to different linear sy…
Thermodynamically consistent physics-informed neural networks for hyperbolic systems
Ravi G. Patel, Indu Manickam, Nathaniel A. Trask +4
Physics-informed neural network architectures have emerged as a powerful tool for developing flexible PDE solvers which easily assimilate data, but face challenges related to the P…
Monolithic Multigrid for Magnetohydrodynamics
J. H. Adler, T. Benson, E. C. Cyr +3
The magnetohydrodynamics (MHD) equations model a wide range of plasma physics applications and are characterized by a nonlinear system of partial differential equations that strong…