Tareq Uz Zaman — accepted AI-conference papers

2023

MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods

Ali Taghibakhshi, Nicolas Nytko, Tareq Uz Zaman, Scott MacLachlan, Luke Olson, Matthew West

ICML 2023poster

Domain decomposition methods (DDMs) are popular solvers for discretized systems of partial differential equations (PDEs), with one-level and multilevel variants. These solvers rely on several algorithmic and mathematical parameters, prescribing overlap, subdomain boundary conditions, and other prope…

Cited by 15 Source ↗PDF ↗code ↗Scholar ↗

2022

Learning Interface Conditions in Domain Decomposition Solvers

Ali Taghibakhshi, Nicolas Nytko, Tareq Uz Zaman, Scott MacLachlan, Luke Olson, Matthew West

NeurIPS 2022accept

Domain decomposition methods are widely used and effective in the approximation of solutions to partial differential equations. Yet the \textit{optimal} construction of these methods requires tedious analysis and is often available only in simplified, structured-grid settings, limiting their use fo…

Cited by 14 Source ↗PDF ↗code ↗Scholar ↗