通过机器学习增强计算流体动力学
Ricardo Vinuesa1,2, Steven L Brunton3
1FLOW, Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden. rvinuesa@mech.kth.se.
Nature computational science
|January 4, 2024
概括
机器学习 (ML) 在计算流体动力学 (CFD) 中加速科学计算. 尽管有一些局限性,但ML显示了模拟,流建模和减少顺序模型的高潜力.
科学领域:
- 计算流体动力学 计算流体动力学
- 科学计算是科学计算.
- 机器学习是机器学习.
背景情况:
- 机器学习 (ML) 越来越多地成为科学计算的组成部分.
- 计算流体动力学 (CFD) 为机器学习应用提供了重大机遇.
研究的目的:
- 在CFD中突出 ML 的高影响区域.
- 讨论新兴的ML技术和CFD的潜在限制.
主要方法:
- 审查目前在CFD中的ML应用.
- 确定未来研发的关键领域.
主要成果:
- ML可以加速直接的数值模拟.
- ML为流关闭建模提供了改进.
- ML有助于开发增强的减少顺序模型.
结论:
- ML对推进CFD有很大的前景.
- 考虑新兴的ML领域和局限性对于成功的整合至关重要.
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