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Found 5 papers, 4 papers with code
Learning from Integral Losses in Physics Informed Neural Networks
Our numerical results confirm the existence of the aforementioned bias in practice, and also show that our proposed delayed target approach can lead to accurate solutions with comparable quality to ones estimated with a large number of samples.
MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods
Domain decomposition methods (DDMs) are popular solvers for discretized systems of partial differential equations (PDEs), with one-level and multilevel variants.
Learning Interface Conditions in Domain Decomposition Solvers
Domain decomposition methods are widely used and effective in the approximation of solutions to partial differential equations.
Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning
A system of linear equations defines a graph on the set of unknowns and each level of a multigrid solver requires the selection of an appropriate coarse graph along with restriction and interpolation operators that map to and from the coarse representation.
Learning with Analytical Models
To understand and predict the performance of scientific applications, several analytical and machine learning approaches have been proposed, each having its advantages and disadvantages.
