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arXiv 2207.03239cond-mat.mtrl-sci
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基于傅里叶神经网络的数字复合材料应力应变场学习

Learning the Stress-Strain Fields in Digital Composites using Fourier Neural Operator

Meer Mehran Rashid, Tanu Pittie, Souvik Chakraborty, N. M. Anoop Krishnan

摘要:

对高性能材料的需求不断增加,导致了具有复杂分层设计的先进复合材料。然而,设计具有目标特性和性能的定制材料微结构是极具挑战性的,因为基于物理的求解器有无数的设计组合和高昂的计算成本。在这项研究中,我们采用基于神经算子的框架,即傅立叶神经算子(FNO)学习二维复合材料的力学响应。我们表明,FNO表现出高保真度的预测的完整的应力和应变张量场的几何复杂的复合材料微观结构,很少的训练数据,纯粹的微观结构的基础上。该模型还表现出zero-shot推广的不可见的任意几何形状的高精度。此外,该模型表现出zero-shot超分辨率的能力,预测高分辨率的应力和应变场直接从低分辨率的输入配置。最后,该模型还提供了高精度的预测等效措施的应力-应变场允许现实的放大的结果。

英文摘要:

Increased demands for high-performance materials have led to advanced composite materials with complex hierarchical designs. However, designing a tailored material microstructure with targeted properties and performance is extremely challenging due to the innumerable design combinations and prohibitive computational costs for physics-based solvers. In this study, we employ a neural operator-based framework, namely Fourier neural operator (FNO) to learn the mechanical response of 2D composites. We show that the FNO exhibits high-fidelity predictions of the complete stress and strain tensor fields for geometrically complex composite microstructures with very few training data and purely based on the microstructure. The model also exhibits zero-shot generalization on unseen arbitrary geometries with high accuracy. Furthermore, the model exhibits zero-shot super-resolution capabilities by predicting high-resolution stress and strain fields directly from low-resolution input configurations. Finally, the model also provides high-accuracy predictions of equivalent measures for stress-strain fields allowing realistic upscaling of the results.

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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