Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling

[PDF] arxiv.org

Active Learning for Neural PDE Solvers

D Musekamp, M Kalimuthu, D Holzmüller… - arXiv preprint arXiv …, 2024 - arxiv.org
Solving partial differential equations (PDEs) is a fundamental problem in engineering and
science. While neural PDE solvers can be more efficient than established numerical solvers …
被引用次数：1 相关文章 所有 6 个版本
[PDF] arxiv.org

Accelerating PDE Data Generation via Differential Operator Action in Solution Space

H Dong, H Wang, H Liu, J Luo, J Wang - arXiv preprint arXiv:2402.05957, 2024 - arxiv.org
Recent advancements in data-driven approaches, such as Neural Operator (NO), have
demonstrated their effectiveness in reducing the solving time of Partial Differential Equations …
相关文章 所有 3 个版本