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arXiv 2201.06780math.APcs.LGphysics.flu-dyn
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三维轴对称欧拉方程渐近自相似爆破剖面的神经网络求解

Asymptotic self-similar blow-up profile for three-dimensional axisymmetric Euler equations using neural networks

Princeton University(普林斯顿大学);Brown University(布朗大学);Universitat de Barcelona(巴塞罗那大学);Centre de Recerca Matemàtica(数学研究中心);Institute for Advanced Study(高等研究院)

Yongji Wang, Ching-Yao Lai, Javier Gómez-Serrano, Tristan Buckmaster

AI总结:

本文用物理信息神经网络首次发现二维Boussinesq与三维Euler方程的光滑自相似爆破剖面,并构造了Córdoba-Córdoba-Fontelos方程的首个不稳定自相似解,为计算机辅助爆破证明奠定基础。

摘要:

二维Boussinesq方程和三维Euler方程是否存在有限时间爆破解是流体力学领域的基本问题。我们开发了一种新的数值框架,采用物理信息神经网络(PINNs),首次发现这两个方程都存在光滑的自相似爆破剖面。该解本身可为未来对这两个方程爆破性的计算机辅助证明奠定基础。此外,我们通过构造Córdoba-Córdoba-Fontelos方程的第一个不稳定自相似解,证明了PINNs可成功应用于寻找流体方程的不稳定自相似解。我们表明,我们的数值框架具有鲁棒性,并可适用于各种其他方程。

英文摘要:

Whether there exist finite time blow-up solutions for the 2-D Boussinesq and the 3-D Euler equations are of fundamental importance to the field of fluid mechanics. We develop a new numerical framework, employing physics-informed neural networks (PINNs), that discover, for the first time, a smooth self-similar blow-up profile for both equations. The solution itself could form the basis of a future computer-assisted proof of blow-up for both equations. In addition, we demonstrate PINNs could be successfully applied to find unstable self-similar solutions to fluid equations by constructing the first example of an unstable self-similar solution to the Córdoba-Córdoba-Fontelos equation. We show that our numerical framework is both robust and adaptable to various other equations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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