General Covariance Data Augmentation for Neural PDE Solvers. - Vladimir Fanaskov; Tianchi Yu; Alexander Rudikov; Ivan V. Oseledets (ICML, 2023)

Neural operators meet conjugate gradients: The FCG-NO method for efficient PDE solving. - Alexander Rudikov; Vladimir Fanaskov; Ekaterina A. Muravleva; Yuri M. Laevsky; Ivan V. Oseledets (ICML, 2024)

Quantization of Large Language Models with an Overdetermined Basis. - Daniil Merkulov; Daria Cherniuk; Alexander Rudikov; Ivan V. Oseledets; Ekaterina A. Muravleva; Aleksandr Mikhalev; Boris Kashin (UAI, 2024)

Neural functional a posteriori error estimates. - Vladimir Fanaskov; Alexander Rudikov; Ivan V. Oseledets (CoRR, 2024)

Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers. - Vladislav Trifonov; Alexander Rudikov; Oleg Iliev 001; Ivan V. Oseledets; Ekaterina A. Muravleva (CoRR, 2024)

Astral: training physics-informed neural networks with error majorants. - Vladimir Fanaskov; Tianchi Yu; Alexander Rudikov; Ivan V. Oseledets (CoRR, 2024)

ConDiff: A Challenging Dataset for Neural Solvers of Partial Differential Equations. - Vladislav Trifonov; Alexander Rudikov; Oleg Iliev 001; Ivan V. Oseledets; Ekaterina A. Muravleva (CoRR, 2024)

Locally Subspace-Informed Neural Operators for Efficient Multiscale PDE Solving. - Alexander Rudikov; Vladimir Fanaskov; Sergei Stepanov; Buzheng Shan; Ekaterina A. Muravleva; Yalchin Efendiev; Ivan V. Oseledets (CoRR, 2025)