arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2403.06342math.NAcs.LGcs.NA
收藏

用于求解玻尔兹曼方程BGK模型的可分离物理信息神经网络

Separable Physics-informed Neural Networks for Solving the BGK Model of the Boltzmann Equation

Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology (KAIST)(韩国科学技术院数学科学系);Department of Mathematics, Gyeongsang National University(庆尚国立大学数学系);Department of Mathematics, Sungkyunkwan University(全州大学数学系);Department of Electrical and Computer Engineering, Sungkyunkwan University(全州大学电子与计算机工程系)

Jaemin Oh, Seung Yeon Cho, Seok-Bae Yun, Eunbyung Park, Youngjoon Hong

AI总结:

提出基于可分离物理信息神经网络(SPINNs)的方法求解玻尔兹曼方程BGK模型,利用分解结构降低计算成本,结合高斯函数与相对损失提升多尺度特征逼近精度,五项实验验证了其高效性与准确性。

摘要:

在本研究中,我们引入了一种基于可分离物理信息神经网络(SPINNs)的方法,以有效求解玻尔兹曼方程的BGK模型。尽管PINNs的无网格特性在处理高维偏微分方程(PDEs)时具有显著优势,但在应用求积规则对BGK算子进行精确积分评估时出现了挑战,这可能损害无网格的益处并增加计算成本。为此,我们利用SPINNs的规范多项式分解结构以及矩计算的线性特性,大幅降低了应用求积规则的计算开销。粒子密度函数的多尺度特性导致难以使用神经网络精确逼近宏观矩。为改进SPINN训练,我们引入了将高斯函数集成到SPINNs中的方法,并结合相对损失方法。此修改使SPINNs能够像麦克斯韦分布一样快速衰减,从而提高了宏观矩逼近的精度。相对损失设计进一步确保SPINNs能有效捕捉大尺度和小尺度特征。通过包括具有挑战性的3D Riemann问题在内的五项数值实验,我们展示了该方法的有效性。这些结果凸显了这种新方法在高效准确解决计算物理复杂挑战方面的潜力。

英文摘要:

In this study, we introduce a method based on Separable Physics-Informed Neural Networks (SPINNs) for effectively solving the BGK model of the Boltzmann equation. While the mesh-free nature of PINNs offers significant advantages in handling high-dimensional partial differential equations (PDEs), challenges arise when applying quadrature rules for accurate integral evaluation in the BGK operator, which can compromise the mesh-free benefit and increase computational costs. To address this, we leverage the canonical polyadic decomposition structure of SPINNs and the linear nature of moment calculation, achieving a substantial reduction in computational expense for quadrature rule application. The multi-scale nature of the particle density function poses difficulties in precisely approximating macroscopic moments using neural networks. To improve SPINN training, we introduce the integration of Gaussian functions into SPINNs, coupled with a relative loss approach. This modification enables SPINNs to decay as rapidly as Maxwellian distributions, thereby enhancing the accuracy of macroscopic moment approximations. The relative loss design further ensures that both large and small-scale features are effectively captured by the SPINNs. The efficacy of our approach is demonstrated through a series of five numerical experiments, including the solution to a challenging 3D Riemann problem. These results highlight the potential of our novel method in efficiently and accurately addressing complex challenges in computational physics.

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。

↑