论文标题
部分可观测时空混沌系统的无模型预测
Emerging trends in machine learning for computational fluid dynamics
论文作者
论文摘要
科学界对机器学习（ML）的新兴趣正在开放许多新的研究领域。在这里，我们关注ML的新趋势如何提供改善计算流体动力学领域（CFD）的机会。特别是，我们讨论已经显示出好处的ML和CFD之间的协同作用，我们还评估正在开发的领域，并可能在未来几年产生重要的好处。我们认为，强调对这些新兴方法的谨慎乐观的平衡观点也很重要
The renewed interest from the scientific community in machine learning (ML) is opening many new areas of research. Here we focus on how novel trends in ML are providing opportunities to improve the field of computational fluid dynamics (CFD). In particular, we discuss synergies between ML and CFD that have already shown benefits, and we also assess areas that are under development and may produce important benefits in the coming years. We believe that it is also important to emphasize a balanced perspective of cautious optimism for these emerging approaches
