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April 1, 2026Computer Modeling in Engineering & Sciences0 citationsOpen Access

A Surrogate Deep-Learning Super-Resolution Framework for Accelerating Finite Element Method-Based Fluid Simulations

SSSojin ShinChangwon National UniversityGKGuk Heon KimChangwon National UniversitySKSeung Hyun KIMChangwon National University

Key Points

  • The research aims to create a deep learning framework that accelerates fluid simulations based on the finite element method.
  • Developed a surrogate super-resolution framework using deep learning techniques
  • Applied the framework to finite element method-based computational fluid dynamics
  • Conducted evaluations to assess computational efficiency and simulation accuracy
  • Demonstrated significant acceleration in fluid simulations
  • Achieved high-resolution results with reduced computational cost
  • Showed improved accuracy in simulations compared to traditional methods

Abstract

This study develops a surrogate super-resolution (SR) framework that accelerates finite element method (FEM)-based computational fluid dynamics (CFD) using deep learning. High-resolution (HR) FEM-based CFD remains computation... | Find, read and cite all the research you need on Tech Science Press

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Cite This Study

Shin et al. (2026) studied this question.

synapsesocial.com/papers/69cd7b345652765b073a90a0https://doi.org/10.32604/cmes.2026.079127
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