Open Access

ARTICLE

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

Sojin Shin¹, Guk Heon Kim², Seung Hwan Kim³, Jaemin Kim^2,*

1 Department of Innovative Divertor Development Group, Korea Institute of Fusion Energy, Daejeon, Republic of Korea
2 Department of Mechanical Engineering, Changwon National University, Changwon, Republic of Korea
3 Department of Neurosurgery, Sungkyunkwan University School of Medicine, Samsung Changwon Hospital, Changwon, Republic of Korea

* Corresponding Author: Jaemin Kim. Email:

(This article belongs to the Special Issue: Machine Learning, Data-Driven and Novel Approaches in Computational Mechanics)

Computer Modeling in Engineering & Sciences 2026, 146(3), 21 https://doi.org/10.32604/cmes.2026.079127

Received 15 January 2026; Accepted 15 January 2026; Issue published 30 March 2026

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 computationally prohibitive for time-sensitive applications, including patient-specific aneurysm hemodynamics where rapid turnaround is valuable. The proposed pipeline learns to reconstruct HR velocity-magnitude fields from low-resolution (LR) FEM solutions generated under the same governing equations and boundary conditions. It consists of three modules: (i) offline pre-training of a residual network on representative vascular geometries; (ii) lightweight fine-tuning to adapt the pretrained model to geometric variability, including patient-specific aneurysm morphologies; and (iii) an unstructured-to-structured sampling strategy with region-of-interest upsampling that concentrates resolution in flow-critical zones (e.g., the aneurysm sac) rather than the full domain. This targeted reconstruction substantially reduces inference and post-processing cost while preserving key HR flow features. Experiments on cerebral aneurysm models show that HR velocity-magnitude fields can be recovered with accuracy comparable to direct HR simulations at less than 1% of the direct HR simulation cost per analysis (LR simulation and SR inference), while adaptation to new geometries requires only lightweight fine-tuning with limited target-specific HR data. While clinical endpoints and additional variables (e.g., pressure or wall-based metrics) are left for future work, the results indicate that the proposed surrogate SR approach can streamline FEM-based CFD workflows toward near real-time hemodynamic analysis across morphologically similar vascular models.

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Keywords

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