Machine learning modeling based on informer for predicting complex unsteady flow fields to reduce consumption of computational fluid dynamics simulation
Accurately predicting the dynamic behaviour of complex flow fields has always been a major challenge in Computational Fluid Dynamics (CFD) research. This paper proposes an innovative approach based on the Informer model for efficient prediction of unsteady flow fields. This study focuses on the two-...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2024.2443118 |
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