Papers › Machine Learning and Finite Element Method for Physical Systems Modeling
Machine Learning and Finite Element Method for Physical Systems Modeling
O. Kononenko, I. Kononenko
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Modeling of physical systems includes extensive use of software packages that implement the accurate finite element method for solving differential equations considered along with the appropriate initial and boundary conditions. When the problem size becomes large, time needed to solve the resulting linear systems may range from hours to weeks, and if the input parameters need to be adjusted, even slightly, the simulations has to be re-done from scratch. Recent advances in machine learning algorithms and their successful applications in various fields demonstrate that, if properly chosen and trained, these models can significantly improve conventional techniques. In this note we discuss possibilities to complement the finite element studies with machine learning and provide several basic examples.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections