Quasi-optimal hp-finite element refinements towards singularities via deep neural network prediction

Służalec, Tomasz; Grzeszczuk, Rafał Wojciech; Rojas, Sergio; Dzwinel, Witold; Paszyński, Maciej

doi:10.1016/j.camwa.2023.04.023

Computers & Mathematics with Applications

2023

DOI: 10.1016/j.camwa.2023.04.023

|View full text |Cite

Quasi-optimal hp-finite element refinements towards singularities via deep neural network prediction

Tomasz Służalec

Rafał Wojciech Grzeszczuk

Sergio Rojas

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Quasi-optimal hp-finite element refinements towards singularities via deep neural network prediction

Cited by 3 publications

References 29 publications

Learning Topological Operations on Meshes with Application to Block Decomposition of Polygons

Learning Topological Operations on Meshes with Application to Block Decomposition of Polygons

DynAMO: Multi-agent reinforcement learning for dynamic anticipatory mesh optimization with applications to hyperbolic conservation laws

Deep Fourier Residual method for solving time-harmonic Maxwell's equations

Contact Info

Product

Resources

About