90 citations · 116 across the 16 of their papers we have counts for
7 papers · 1 filter
PERELMAN: Pipeline for scientific literature meta-analysis. Technical report
Daniil Sherki, Daniil Merkulov, Alexandra Savina +1
We present PERELMAN (PipEline foR sciEntific Literature Meta-ANalysis), an agentic framework designed to extract specific information from a large corpus of scientific articles to…
Spectral Analysis of the Weighted Frobenius Objective
Vladislav Trifonov, Ivan Oseledets, Ekaterina Muravleva
We analyze a weighted Frobenius loss for approximating symmetric positive definite matrices in the context of preconditioning iterative solvers. Unlike the standard Frobenius norm,…
Deep Learning for Subspace Regression
Vladimir Fanaskov, Vladislav Trifonov, Alexander Rudikov +2
It is often possible to perform reduced order modelling by specifying linear subspace which accurately captures the dynamics of the system. This approach becomes especially appeali…
Geological Field Restoration through the Lens of Image Inpainting
Vladislav Trifonov, Ivan Oseledets, Ekaterina Muravleva
We study an ill-posed problem of geological field reconstruction under limited observations. Engineers often have to deal with the problem of reconstructing the subsurface geologic…
Locally Subspace-Informed Neural Operators for Efficient Multiscale PDE Solving
Alexander Rudikov, Vladimir Fanaskov, Sergei Stepanov +4
Neural operators (NOs) struggle with high-contrast multiscale partial differential equations (PDEs), where fine-scale heterogeneities cause large errors. To address this, we use th…
Bayesian Inverse Problems Meet Flow Matching: Efficient and Flexible Inference via Transformers
Daniil Sherki, Ivan Oseledets, Ekaterina Muravleva
The efficient resolution of Bayesian inverse problems remains challenging due to the high computational cost of traditional sampling methods. In this paper, we propose a novel fram…
Message-Passing GNNs Fail to Approximate Sparse Triangular Factorizations
Vladislav Trifonov, Ekaterina Muravleva, Ivan Oseledets
Graph Neural Networks (GNNs) have been proposed as a tool for learning sparse matrix preconditioners, which are key components in accelerating linear solvers. We present theoretica…