HADF：面向湍流重构的哈希自适应双融合隐式网络

刘云飞; 陈新海; 张根; 张庆阳; 王庆林; 刘杰

doi:10.1631/FITEE.2500419

Your Location：

Home >

Browse articles >

HADF：面向湍流重构的哈希自适应双融合隐式网络

常规文章 | Updated：2026-01-07

- HADF：面向湍流重构的哈希自适应双融合隐式网络
  Enhanced Publication
- HADF: a hash-adaptive dual fusion implicit network for super-resolution of turbulent flows
- “在流体动力学领域，从稀疏测量重建高保真湍流表示方面取得了突破。专家研究人员开发了HADF系统，该系统为解决高计算成本和多尺度重建局限性的挑战提供了解决方案。”
- Frontiers of Information Technology & Electronic Engineering 2025年26卷第11期页码：2159-2175
- Affiliations：
  
  1.National Key Laboratory of Parallel and Distributed Computing, National University of Defense Technology, Changsha 410073, China
  2.Laboratory of Digitizing Software for Frontier Equipment, National University of Defense Technology, Changsha 410073, China
  3.College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China
- Author bio：
  
  E-mail: liuyunfei@nudt.edu.cn
  ‡ Corresponding author
- Funds：
  
  National Natural Science Foundation of China(12402349);Natural Science Foundation of Hunan Province(2024JJ6468);Youth Foundation of National University of Defense Technology, China(ZK2023-11);National Key Research and Development Program of China(2021YFB0300101)
- DOI：10.1631/FITEE.2500419
  中图分类号： TP391.4;O35
- 收稿：2025-06-17，
  
  修回：2025-10-24，
  
  网络出版：2025-12-08，
  
  纸质出版：2025-11
- Accepted：
Scan QR Code
刘云飞, 陈新海, 张根, 等. HADF：面向湍流重构的哈希自适应双融合隐式网络[J]. Frontiers of Information Technology & Electronic Engineering, 2025,26(11):2159-2175.

Yunfei LIU, Xinhai CHEN, Gen ZHANG, et al. HADF: a hash-adaptive dual fusion implicit network for super-resolution of turbulent flows[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(11): 2159-2175.
刘云飞, 陈新海, 张根, 等. HADF：面向湍流重构的哈希自适应双融合隐式网络[J]. Frontiers of Information Technology & Electronic Engineering, 2025,26(11):2159-2175. DOI： 10.1631/FITEE.2500419.

Yunfei LIU, Xinhai CHEN, Gen ZHANG, et al. HADF: a hash-adaptive dual fusion implicit network for super-resolution of turbulent flows[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(11): 2159-2175. DOI： 10.1631/FITEE.2500419.

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

QuantBench: benchmarking AI methods for quantitative investment from a full pipeline perspective

MltAuxTSPP: a unified benchmark for deep learning-based traffic state prediction with multi-source auxiliary data

Q-space-coordinate-guided neural networks for high-fidelity diffusion tensor estimation from minimal diffusion-weighted images

CRGT-SA: an interlaced and spatiotemporal deep learning model for network intrusion detection

Federated model with contrastive learning and adaptive control variates for human activity recognition

HADF：面向湍流重构的哈希自适应双融合隐式网络

HADF: a hash-adaptive dual fusion implicit network for super-resolution of turbulent flows

DOI：10.1631/FITEE.2500419