Leverage Gaussian Interpolation Features Extraction and Advanced Encode–Decode Models for 2D Steady State Flows Estimation
Accurate estimation of two-dimensional (2D) multi-obstacle steady-state flows is crucial in various scientific and engineering disciplines, yet conventional methods often fall short in precision and computational efficiency. This paper introduces a novel approach that combines Gaussian interpolation...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11071317/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839633650127208448 |
|---|---|
| author | Thi-Thu-Huong Le Changwoo Choi Junyoung Son Howon Kim |
| author_facet | Thi-Thu-Huong Le Changwoo Choi Junyoung Son Howon Kim |
| author_sort | Thi-Thu-Huong Le |
| collection | DOAJ |
| description | Accurate estimation of two-dimensional (2D) multi-obstacle steady-state flows is crucial in various scientific and engineering disciplines, yet conventional methods often fall short in precision and computational efficiency. This paper introduces a novel approach that combines Gaussian interpolation-based feature extraction with advanced encode-decode models, including AutoEncoder, UNet, and DeepLabV3+. By leveraging Gaussian interpolation and advanced Encoder-Decoder models, we effectively capture the smooth, continuous nature of steady-state flows while mitigating noise and small-scale fluctuations that may obscure global flow patterns. We evaluate our method on a private dataset of 2D multi-obstacle steady-state flows, comparing the performance of each model based on accuracy, computational efficiency, and robustness. Our results demonstrate that the encode-decode architectures, particularly UNet and DeepLabV3+, significantly improve flow estimation accuracy and feature retention. This study suggests that integrating Gaussian interpolation with advanced encode-decode models offers an efficient and effective solution for complex flow estimation tasks, opening new avenues for research in fluid dynamics applications. |
| format | Article |
| id | doaj-art-1a55d9ef6eae4d8a9bcab85851b5a82d |
| institution | Matheson Library |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1a55d9ef6eae4d8a9bcab85851b5a82d2025-07-10T23:01:17ZengIEEEIEEE Access2169-35362025-01-011311538411540510.1109/ACCESS.2025.358540211071317Leverage Gaussian Interpolation Features Extraction and Advanced Encode–Decode Models for 2D Steady State Flows EstimationThi-Thu-Huong Le0https://orcid.org/0000-0002-8366-9396Changwoo Choi1https://orcid.org/0000-0002-1701-2935Junyoung Son2https://orcid.org/0000-0001-6033-1619Howon Kim3https://orcid.org/0000-0001-8475-7294Blockchain Platform Technology Center, Pusan National University, Busan, South KoreaSchool of Computer Science and Engineering, Pusan National University, Busan, South KoreaSchool of Computer Science and Engineering, Pusan National University, Busan, South KoreaSchool of Computer Science and Engineering, Pusan National University, Busan, South KoreaAccurate estimation of two-dimensional (2D) multi-obstacle steady-state flows is crucial in various scientific and engineering disciplines, yet conventional methods often fall short in precision and computational efficiency. This paper introduces a novel approach that combines Gaussian interpolation-based feature extraction with advanced encode-decode models, including AutoEncoder, UNet, and DeepLabV3+. By leveraging Gaussian interpolation and advanced Encoder-Decoder models, we effectively capture the smooth, continuous nature of steady-state flows while mitigating noise and small-scale fluctuations that may obscure global flow patterns. We evaluate our method on a private dataset of 2D multi-obstacle steady-state flows, comparing the performance of each model based on accuracy, computational efficiency, and robustness. Our results demonstrate that the encode-decode architectures, particularly UNet and DeepLabV3+, significantly improve flow estimation accuracy and feature retention. This study suggests that integrating Gaussian interpolation with advanced encode-decode models offers an efficient and effective solution for complex flow estimation tasks, opening new avenues for research in fluid dynamics applications.https://ieeexplore.ieee.org/document/11071317/AutoEncodercomputational fluid dynamicDeepLabV3+steady state flowsGaussian interpolationUNET |
| spellingShingle | Thi-Thu-Huong Le Changwoo Choi Junyoung Son Howon Kim Leverage Gaussian Interpolation Features Extraction and Advanced Encode–Decode Models for 2D Steady State Flows Estimation IEEE Access AutoEncoder computational fluid dynamic DeepLabV3+ steady state flows Gaussian interpolation UNET |
| title | Leverage Gaussian Interpolation Features Extraction and Advanced Encode–Decode Models for 2D Steady State Flows Estimation |
| title_full | Leverage Gaussian Interpolation Features Extraction and Advanced Encode–Decode Models for 2D Steady State Flows Estimation |
| title_fullStr | Leverage Gaussian Interpolation Features Extraction and Advanced Encode–Decode Models for 2D Steady State Flows Estimation |
| title_full_unstemmed | Leverage Gaussian Interpolation Features Extraction and Advanced Encode–Decode Models for 2D Steady State Flows Estimation |
| title_short | Leverage Gaussian Interpolation Features Extraction and Advanced Encode–Decode Models for 2D Steady State Flows Estimation |
| title_sort | leverage gaussian interpolation features extraction and advanced encode x2013 decode models for 2d steady state flows estimation |
| topic | AutoEncoder computational fluid dynamic DeepLabV3+ steady state flows Gaussian interpolation UNET |
| url | https://ieeexplore.ieee.org/document/11071317/ |
| work_keys_str_mv | AT thithuhuongle leveragegaussianinterpolationfeaturesextractionandadvancedencodex2013decodemodelsfor2dsteadystateflowsestimation AT changwoochoi leveragegaussianinterpolationfeaturesextractionandadvancedencodex2013decodemodelsfor2dsteadystateflowsestimation AT junyoungson leveragegaussianinterpolationfeaturesextractionandadvancedencodex2013decodemodelsfor2dsteadystateflowsestimation AT howonkim leveragegaussianinterpolationfeaturesextractionandadvancedencodex2013decodemodelsfor2dsteadystateflowsestimation |