2 citations · 6 across the 5 of their papers we have counts for
5 papers
Binding threshold units with artificial oscillatory neurons
Vladimir Fanaskov, Ivan Oseledets
Artificial Kuramoto oscillatory neurons were recently introduced as an alternative to threshold units. Empirical evidence suggests that oscillatory units outperform threshold units…
Neural operators meet conjugate gradients: The FCG-NO method for efficient PDE solving
Alexander Rudikov, Vladimir Fanaskov, Ekaterina Muravleva +2
Deep learning solvers for partial differential equations typically have limited accuracy. We propose to overcome this problem by using them as preconditioners. More specifically, w…
Neural functional a posteriori error estimates
Vladimir Fanaskov, Alexander Rudikov, Ivan Oseledets
We propose a new loss function for supervised and physics-informed training of neural networks and operators that incorporates a posteriori error estimate. More specifically, durin…
Neural Multigrid Architectures
Vladimir Fanaskov
We propose a convenient matrix-free neural architecture for the multigrid method. The architecture is simple enough to be implemented in less than fifty lines of code, yet it encom…
Uncertainty calibration for probabilistic projection methods
Vladimir Fanaskov
Classical Krylov subspace projection methods for the solution of linear problem output an approximate solution . Recently, it has been recognized th…