June 10, 2024, 4:45 a.m. | Vladislav Trifonov, Alexander Rudikov, Oleg Iliev, Ivan Oseledets, Ekaterina Muravleva

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.04709v1 Announce Type: new
Abstract: We present ConDiff, a novel dataset for scientific machine learning. ConDiff focuses on the diffusion equation with varying coefficients, a fundamental problem in many applications of parametric partial differential equations (PDEs). The main novelty of the proposed dataset is that we consider discontinuous coefficients with high contrast. These coefficient functions are sampled from a selected set of distributions. This class of problems is not only of great academic interest, but is also the basis for …

abstract applications arxiv cs.lg dataset differential diffusion equation fundamental machine machine learning novel parametric problem scientific type

Senior Machine Learning Engineer

@ GPTZero | Toronto, Canada

ML/AI Engineer / NLP Expert - Custom LLM Development (x/f/m)

@ HelloBetter | Remote

Senior Research Engineer/Specialist - Motor Mechanical Design

@ GKN Aerospace | Bristol, GB

Research Engineer (Motor Mechanical Design)

@ GKN Aerospace | Bristol, GB

Senior Research Engineer (Electromagnetic Design)

@ GKN Aerospace | Bristol, GB

Associate Research Engineer Clubs | Titleist

@ Acushnet Company | Carlsbad, CA, United States