HMgNO:混合多网格神经运算符,具有部分微分方程的低序数值解法器
Yifan Hu1, Weimin Zhang2, Fukang Yin2
1College of Computer Science and Technology, National University of Defense Technology, Changsha, 410073, PR China; College of Meteorology and Oceanography, National University of Defense Technology, Changsha, 410073, PR China.
概括
我们介绍了一种混合多网格神经运算符 (HMgNO),它结合了快速的低顺序解答器和神经运算符. 这种方法实现了高阶的准确性和计算效率,用于解决部分微分方程.
科学领域:
- 计算数学是指计算数学.
- 科学机器学习科学机器学习
- 数字分析 数字分析
背景情况:
- 部分微分方程 (PDEs) 的传统数值方法在计算成本和准确性之间存在权衡.
- 低阶解决器在计算上是高效的,但缺乏准确性,而高阶解决器在计算上是准确的,但成本高昂.
研究的目的:
- 开发一种新的框架,克服解决 PDE 的准确性-效率权衡.
- 使用神经运算符来提高低序数值解法的准确性.
主要方法:
- 拟议的框架,混合多网格神经运算符 (HMgNO),将一个低序数值解析器与一个多网格神经运算符结合起来.
- 神经操作员纠正低阶解决方案,以在固定的时间步骤大小下实现高阶准确性.
- 该框架具有多功能性,支持各种低阶解决方法,如有限差异和光谱方法.
主要成果:
- 对纳维埃-斯托克斯方程,浅水方程和扩散反应方程的实验证明了卓越的性能.
- HMgNO框架实现了最小的相对误差和最小的光谱偏差.
- 该方法需要很少的模型参数,并提供快速推断速度.
结论:
- HMgNO框架有效地实现了解决 PDE 的高精度和计算效率.
- 这种混合方法为复杂的科学模拟提供了一个有希望的解决方案.
- HMgNO框架在PDEs的数值方法方面取得了重大进展.
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