将有限元素与深度神经运算符连接起来,以快速多尺度建模力学问题
Minglang Yin1,2, Enrui Zhang3, Yue Yu4
1Center for Biomedical Engineering, Brown University, Providence, RI, United States of America.
概括
本研究介绍了一种机器学习方法,使用DeepONet创建高效的多尺度模型. 这种方法显著降低了复杂模拟的计算成本,使多物理系统的预测速度更快.
科学领域:
- 计算科学 计算科学
- 应用数学 应用数学 应用数学
- 机器学习 机器学习
背景情况:
- 多尺度建模对于具有不同尺度的多物理系统至关重要,但高保真模拟在计算上是昂贵的.
- 现有的方法难以应对详细模拟的高成本,特别是对于时间依赖的问题.
- 桥梁不同的模型分辨率 (例如,连续和粒子方法) 提出了重要的计算挑战.
研究的目的:
- 利用机器学习开发一个高效的多尺度建模框架,以降低计算成本.
- 雇佣DeepONet,一个神经操作员,作为昂贵的高保真解决方案的替代品.
- 证明框架能够在新条件下准确预测系统响应的能力.
主要方法:
- 在线训练DeepONet,使用来自精细分辨率解决器的数据来学习精细规模的动态.
- 将训练有素的DeepONet与标准的PDE解决方案相结合,用于实时预测.
- 通过使用静态和时间依赖的基准来验证方法,包括将有限元方法 (FEM) 与神经操作员替代物用于光滑粒子水力学 (SPH) 的合.
主要成果:
- 基于DeepONet的多尺度建模框架显著降低了计算成本.
- DeepONet准确地捕捉了微小规模的动态,并为新的预测做出了很好的概括.
- 证明了连续 (FEM) 和基于粒子 (SPH代理) 的模型的成功合,用于材料响应预测.
结论:
- 机器学习,特别是DeepONet,在多尺度建模中为高保真度解决器提供了一个高效的替代品.
- 拟议的框架可为复杂的多物理问题提供具有成本效益和准确的预测.
- 这种方法促进了各种建模技术和接口条件的整合.
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