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Learning an Optimised Stable Taylor-Galerkin Convection Scheme Based on a Local Spectral Model for the Numerical Error Dynamics

JOURNAL OF COMPUTATIONAL PHYSICS(2023)

CERFACS | Braude Coll Engn

Cited 2|Views25
Abstract
We present a new spectral framework to design and optimise numerical methods for convection problems termed Local Transfer function Analysis (LTA), which improves the state-of-art model for error dynamics due to numerical discretisation. LTA converts the numerical discretisation into a network of transfer function blocks in the spectral space, where optimisation of the solution error becomes an impedance matching problem. In addition, a machine learning model based on the graph neural network is employed to learn the optimal coefficients of the transfer function leading to the matched impedance by examining both the local physical field and the local mesh topology. We demonstrate applications of this technique to design Taylor-Galerkin finite element numerical schemes for solving linear convection on irregular meshes in 1D. Using the LTA, we identify and constrain the neural network predictions to the numerically stable design space, thus improving the stability and generalisation of the optimal parameters that lead to a matched impedance.
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Key words
Taylor-Galerkin scheme,Machine learning,Numerical analysis,Finite-element method
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要点】:本文提出了一种新的谱框架——局部传输函数分析(LTA),通过优化数值方法来解决对流问题中的误差动态,并利用图神经网络学习传输函数的最优系数,实现了Taylor-Galerkin有限元素数值方案的稳定性和误差匹配。

方法】:作者通过将数值离散化转化为频域中的传输函数块网络,并将误差优化问题转化为阻抗匹配问题,采用图神经网络学习最优系数,结合局部物理场和局部网格拓扑。

实验】:研究以一维不规则网格上的线性对流问题为对象,使用LTA技术约束神经网络预测,确保数值稳定性,并成功设计出具有良好稳定性和泛化能力的Taylor-Galerkin有限元素方案,但具体数据集名称未提及。