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在复杂几何形状周围进行流量预测的基准科学机器学习方法
Ali Rabeh1, Ethan Herron1, Aditya Balu1
1Iowa State University, Ames, IA, USA.
Communications engineering
|November 1, 2025
概括
新的基础模型在复杂形状的流体动力学模拟中表现出色,性能优于传统的神经运算符. 像二进制面具和标记距离场 (SDF) 这样的几何表示会影响模型性能,但通用化仍然是一个挑战.
科学领域:
- 计算流体动力学 计算流体动力学
- 科学机器学习 (SciML) 是指科学机器学习.
背景情况:
- 精确的流体动力学模拟对于工程和科学至关重要.
- 当前的SciML模型通常仅限于简单的几何形状,阻碍了具有复杂形状的应用.
研究的目的:
- 在复杂的几何形状上对各种SciML模型进行流体流量预测的基准测试.
- 评估几何表示 (SDF和二进制面具) 对模型性能的影响.
- 引入一个统一的评分框架来评估流体动力学的SciML模型.
主要方法:
- 基准测试神经操作员和基于视觉变压器的基础模型.
- 在复杂的几何形状上利用稳定状态流量的高保真数据集.
- 评估标记距离场 (SDF) 和二进制面具作为几何表示.
主要成果:
- 基础模型显著优于神经运算符,特别是在数据有限的环境中.
- 二元面具可提高视觉变压器性能高达10%;SDF可提高神经操作员性能高达7%.
- 所有测试的模型都在分布之外的概括上表现出局限性.
结论:
- 基础模型代表了SciML在复杂流体动力学方面的重大进步.
- 几何表示的选择极大地影响了模型的性能.
- 在分布之外的泛化仍然是未来SciML在这个领域的发展的一个关键挑战.
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