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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.08021 |
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| _version_ | 1866912422153945088 |
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| author | Zou, Weihao Feng, Weibing Wu, Pin |
| author_facet | Zou, Weihao Feng, Weibing Wu, Pin |
| contents | This study proposes a universal flow field prediction framework based on knowledge transfer
from large language model (LLM), addressing the high computational costs of traditional
computational fluid dynamics (CFD) methods and the limited cross-condition transfer capability
of existing deep learning models. The framework innovatively integrates Proper Orthogonal
Decomposition (POD) dimensionality reduction with fine-tuning strategies for pretrained LLM,
where POD facilitates compressed representation of flow field features while the fine-tuned model
learns to encode system dynamics in state space. To enhance the model's adaptability to flow field
data, we specifically designed fluid dynamics-oriented text templates that improve predictive
performance through enriched contextual semantic information. Experimental results demonstrate
that our framework outperforms conventional Transformer models in few-shot learning scenarios while
exhibiting exceptional generalization across various inflow conditions and airfoil geometries.
Ablation studies reveal the contributions of key components in the FlowBERT architecture. Compared
to traditional Navier-Stokes equation solvers requiring hours of computation, our approach reduces
prediction time to seconds while maintaining over 90% accuracy. The developed knowledge transfer
paradigm establishes a new direction for rapid fluid dynamics prediction, with potential
applications extending to aerodynamic optimization, flow control, and other engineering domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08021 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FlowBERT: Prompt-tuned BERT for variable flow field prediction Zou, Weihao Feng, Weibing Wu, Pin Machine Learning Fluid Dynamics This study proposes a universal flow field prediction framework based on knowledge transfer from large language model (LLM), addressing the high computational costs of traditional computational fluid dynamics (CFD) methods and the limited cross-condition transfer capability of existing deep learning models. The framework innovatively integrates Proper Orthogonal Decomposition (POD) dimensionality reduction with fine-tuning strategies for pretrained LLM, where POD facilitates compressed representation of flow field features while the fine-tuned model learns to encode system dynamics in state space. To enhance the model's adaptability to flow field data, we specifically designed fluid dynamics-oriented text templates that improve predictive performance through enriched contextual semantic information. Experimental results demonstrate that our framework outperforms conventional Transformer models in few-shot learning scenarios while exhibiting exceptional generalization across various inflow conditions and airfoil geometries. Ablation studies reveal the contributions of key components in the FlowBERT architecture. Compared to traditional Navier-Stokes equation solvers requiring hours of computation, our approach reduces prediction time to seconds while maintaining over 90% accuracy. The developed knowledge transfer paradigm establishes a new direction for rapid fluid dynamics prediction, with potential applications extending to aerodynamic optimization, flow control, and other engineering domains. |
| title | FlowBERT: Prompt-tuned BERT for variable flow field prediction |
| topic | Machine Learning Fluid Dynamics |
| url | https://arxiv.org/abs/2506.08021 |