6 citations · 13 across the 6 of their papers we have counts for
4 papers · 1 filter
Learning from Integral Losses in Physics Informed Neural Networks
Ehsan Saleh, Saba Ghaffari, Timothy Bretl +2
This work proposes a solution for the problem of training physics-informed networks under partial integro-differential equations. These equations require an infinite or a large num…
MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods
Ali Taghibakhshi, Nicolas Nytko, Tareq Uz Zaman +3
Domain decomposition methods (DDMs) are popular solvers for discretized systems of partial differential equations (PDEs), with one-level and multilevel variants. These solvers rely…
Learning Interface Conditions in Domain Decomposition Solvers
Ali Taghibakhshi, Nicolas Nytko, Tareq Zaman +3
Domain decomposition methods are widely used and effective in the approximation of solutions to partial differential equations. Yet the optimal construction of these methods requir…
Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning
Ali Taghibakhshi, Scott MacLachlan, Luke Olson +1
Large sparse linear systems of equations are ubiquitous in science and engineering, such as those arising from discretizations of partial differential equations. Algebraic multigri…