HiDeNN-FEM:一种无机器学习方法,用于非线性有限元分析
Yingjian Liu1, Chanwook Park2, Ye Lu2
1Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA.
概括
本研究介绍了使用层次深度学习神经网络 (HiDeNN-FEM) 的非线性有限元素方法. 与传统的有限元素方法相比,这种新的方法显著提高了精度,并减少了数字模拟中的元素扭曲.
科学领域:
- 计算力学 计算力学 计算力学
- 在工程领域的人工智能.
- 数字分析 数字分析
背景情况:
- 传统的有限元素方法 (FEM) 在复杂的模拟中面临着精度和元素扭曲的挑战.
- 层次深度学习神经网络 (HiDeNN) 为微分方程中的数值近似提供了一个系统的方法.
研究的目的:
- 提出基于HiDeNN近似 (非线性HiDeNN-FEM) 的非线性有限元素分析的框架.
- 集成深度神经网络构建块,以进行增强的数值计算.
主要方法:
- 开发了一个非线性HiDeNN-FEM框架,利用结构化的深度神经网络.
- 实现了三个核心构建块:部分导数运算符,r-适应性和材料导数.
- 采用两步优化方案,以实现重新适应性和与现有的FE解决方案集成.
主要成果:
- 在2D和3D示例中,与常规FEM相比,非线性HiDeNN-FEM具有r适应性的准确性明显更高.
- 显示了元素扭曲的实质性减少.
- 有效地抑制了沙钟模式,这是FEM中常见的工件.
结论:
- 拟议的非线性HiDeNN-FEM框架为解决复杂的工程问题提供了强大而准确的替代方案.
- 深度学习的整合增强了传统有限元分析的能力,提高了模拟的可靠性和效率.
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