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!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2169-3536 |