| Neural operators meet conjugate gradients: The FCG-NO method for efficient PDE solving A Rudikov, V Fanaskov, E Muravleva, YM Laevsky, I Oseledets arXiv preprint arXiv:2402.05598, 2024 | 3 | 2024 |
| Neural functional a posteriori error estimates V Fanaskov, A Rudikov, I Oseledets arXiv preprint arXiv:2402.05585, 2024 | 2 | 2024 |
| Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers V Trifonov, A Rudikov, O Iliev, I Oseledets, E Muravleva arXiv preprint arXiv:2405.15557, 2024 | 1 | 2024 |
| General covariance data augmentation for neural PDE solvers V Fanaskov, T Yu, A Rudikov, I Oseledets International Conference on Machine Learning, 9665-9688, 2023 | 1 | 2023 |
| ConDiff: A Challenging Dataset for Neural Solvers of Partial Differential Equations V Trifonov, A Rudikov, O Iliev, I Oseledets, E Muravleva arXiv preprint arXiv:2406.04709, 2024 | | 2024 |
| Astral: training physics-informed neural networks with error majorants V Fanaskov, T Yu, A Rudikov, I Oseledets arXiv preprint arXiv:2406.02645, 2024 | | 2024 |
| Quantization of Large Language Models with an Overdetermined Basis D Merkulov, D Cherniuk, A Rudikov, I Oseledets, E Muravleva, A Mikhalev, ... arXiv preprint arXiv:2404.09737, 2024 | | 2024 |
| General Covariance Data Augmentation for Neural PDE Solvers F Vladimir, T Yu, A Rudikov, I Oseledets | | |
Ivan OseledetsAIRI; Skolkovo Institute of Science and Technology在 skoltech.ru 的电子邮件经过验证
