arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2012.05290math.NAcs.NA
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深层神经网络多重网格求解器的结构保持性

Structure Preservation for the Deep Neural Network Multigrid Solver

Nils Margenberg, Christian Lessig, Thomas Richter

摘要:

偏微分方程的数值模拟是数值分析的中心课题,也是科学、工程及相关领域中不可缺少的工具。现有的方法,如有限元,提供了(高度)有效的工具,但基于深度神经网络的技术在过去几年中作为一种替代方案出现,具有非常有前途的结果。我们调查的组合这两种方法的近似的Navier-斯托克斯方程和在何种程度上的结构特性,如发散自由度可以和应该得到尊重。我们的工作基于DNN-MG,这是我们最近引入的一种深度神经网络多重网格技术,它使用神经网络来表示几何多重网格有限元求解器无法解决的精细网格波动。虽然DNN-MG提供了非常好的精度和计算效率高的解决方案,我们注意到,基于神经网络的校正实质上违反了速度向量场的发散自由。在这篇文章中,我们讨论了这些发现,并分析了三种方法来解决这个问题:一个惩罚项,以鼓励发散自由的网络输出;一个惩罚项的校正速度场;和一个网络,学习的流函数,即标量势的发散自由速度向量场,从而产生建设发散自由校正。我们的实验结果表明,第三种方法的基础上的流函数优于其他两个,不仅提高了发散的自由度,但特别是也整体的模拟保真度。

英文摘要:

The simulation of partial differential equations is a central subject of numerical analysis and an indispensable tool in science, engineering and related fields. Existing approaches, such as finite elements, provide (highly) efficient tools but deep neural network-based techniques emerged in the last few years as an alternative with very promising results. We investigate the combination of both approaches for the approximation of the Navier-Stokes equations and to what extent structural properties such as divergence freedom can and should be respected. Our work is based on DNN-MG, a deep neural network multigrid technique, that we introduced recently and which uses a neural network to represent fine grid fluctuations not resolved by a geometric multigrid finite element solver. Although DNN-MG provides solutions with very good accuracy and is computationally highly efficient, we noticed that the neural network-based corrections substantially violate the divergence freedom of the velocity vector field. In this contribution, we discuss these findings and analyze three approaches to address the problem: a penalty term to encourage divergence freedom of the network output; a penalty term for the corrected velocity field; and a network that learns the stream function, i.e. the scalar potential of the divergence free velocity vector field and which hence yields by construction divergence free corrections. Our experimental results show that the third approach based on the stream function outperforms the other two and not only improves the divergence freedom but in particular also the overall fidelity of the simulation.

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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