Surrogate-Augmented Symbolic CFD-Driven Training Framework for Accelerating Multi-objective Physical Model Development
Published:Dec 22, 2025 05:04
•1 min read
•ArXiv
Analysis
The article describes a research paper on a framework for accelerating the development of physical models. It uses a surrogate-augmented symbolic CFD-driven training approach, suggesting a focus on computational fluid dynamics (CFD) and potentially machine learning techniques to optimize model development. The multi-objective aspect indicates the framework aims to address multiple performance criteria simultaneously.
Key Takeaways
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