NeuberNet:一个神经运算符,通过低保真性弹性模拟在V处解决弹性-塑性部分微分方程
Tommaso Grossi1, Marco Beghini2, Matteo Benedetti3
1TeCIP Institute, Scuola Superiore Sant'Anna, Pisa, Italy. tommaso.grossi@santannapisa.it.
Communications engineering
|December 14, 2025
概括
新型计算框架NeuberNet通过从低保真弹性分析中学习,有效地模拟工程材料中的压力和应变. 这种方法准确地捕捉了几何不规则附近的局部塑料变形,克服了传统方法的局限性.
科学领域:
- 计算力学是计算力学.
- 材料科学是一种材料科学.
- 机器学习 机器学习
背景情况:
- 在具有应力度的工程材料中模拟局部塑性变形是计算密集的.
- 完全非线性模型是准确的,但成本高昂;弹性分析是高效的,但忽略了非线性.
研究的目的:
- 介绍NeuberNet,一个多任务非线性多折解码器,用于高效的小规模可塑性分析.
- 开发一种数据驱动的方法,将低保真弹性模拟映射到高分辨率的弹性-塑料应力和张力场.
主要方法:
- NeuberNet学习了远场移位边界条件和应力/应变场之间的映射.
- 在小规模可塑性和双线性同otropic硬化假设下利用弹性-塑性轴对称固体力学.
- 实现数据驱动的分结构原理,以激活压力升高器附近的可塑性.
主要成果:
- NeuberNet提供计算效率高,可靠的小规模可塑性分析.
- 演示了对轴对称几何和非对称边界条件的3D问题的概括.
- 提供了网格分辨率的指导方针,并确定了违反小规模可塑性假设的情况.
结论:
- NeuberNet提供了一个强大的,计算效率高的框架,用于分析小规模的可塑性.
- 该方法通过将非线性分析集中在关键区域,有效地建模复杂几何形状.
- 在传统方法面临计算或准确性权衡的情况下,NeuberNet提高了模拟的准确性.
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