不稳定的圆柱体从任意的物体中醒来,具有可分化的物理辅助神经网络
Shuvayan Brahmachary1, Nils Thuerey1
1Department of Mathematics and Informatics, Technical University of Munich Boltzmannstrasse 3, 85748 Garching, Germany.
Physical review. E
|June 22, 2024
概括
一个新的混合框架准确地重建了复杂形状周围不稳定的流体流. 这种可微分物理辅助神经网络 (DPNN) 改善了边界层配置,并为各种气配置提高了唤醒动态.
科学领域:
- 流体动力学 流体动力学
- 计算科学 计算科学
- 机器学习 机器学习
背景情况:
- 在多个气周围不稳定的流体流动表现出复杂的动态,包括周期性和混乱的清醒.
- 准确地重建这些流动,保持边界层配置和唤醒动态,对于预测建模至关重要.
研究的目的:
- 开发一种混合预测框架,用于在各种气配置周围重建不稳定的流体流.
- 确保在长时间内准确地保存本地边界层形状和全球复苏动态.
主要方法:
- 一个混合框架,将可微分流量解决器与神经网络相结合,称为可微分物理辅助神经网络 (DPNN).
- 在任意形状的物体上训练DPNN,并在分布之外的样本上进行测试.
主要成果:
- DPNN框架有效地纠正了局部边界层的配置文件,并增强了流场的散射性.
- DPNN显著优于传统的监督学习方法,同时保持了功能空间动态的减少.
- 模型预测与斯特鲁哈尔数分布的现有文献保持一致,并揭示了任意体的类似唤醒类别.
结论:
- DPNN提供了一个强大的工具,用于准确和长期预测复杂的不稳定的流体流.
- 这种方法在进行流量重建任务时,与监督学习相比,显示出更高的性能.
- 该框架有可能为各种身体形状的唤醒动力学发现新的见解.
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