圆式PDE学习被证明是数据效率高的
Nicolas Boullé1, Diana Halikias2, Alex Townsend2
1Isaac Newton Institute for Mathematical Sciences, University of Cambridge, Cambridge CB3 0EH, United Kingdom.
这项研究为部分微分方程 (PDE) 学习提供了理论上的保证,证明了数据效率高的算法可以从有限的数据中恢复物理系统,成功的概率很高.
科学领域:
- 物理与机器学习 物理与机器学习
- 计算数学 计算数学 计算数学
背景情况:
- 部分微分方程 (PDE) 学习将物理和机器学习结合起来,从实验数据中识别物理系统.
- 虽然目前的深度学习模型在有限的数据上表现出色,但它们的成功在很大程度上是经验性的.
- 现有的PDE学习方法缺乏对数据要求的理论保证.
研究的目的:
- 提供PDE学习所需的培训对数量的理论保证.
- 开发一种可证明的数据效率高的算法,用于恢复 PDE 的解决方案运营商.
- 为了建立PDE学习算法的指数趋同率.
主要方法:
- 利用随机的数值线性代数技术.
- 已建立的部分微分方程 (PDE) 理论的应用.
- 从输入-输出数据中推导出一种新的算法,用于解决方案操作员从输入-输出数据中恢复.
主要成果:
- 一个可证明的数据效率高的算法,用于学习三维均圆形PDEs.
- 与训练数据集大小相关的错误的指数趋同率的证明.
- 在恢复未知的物理系统方面取得了非常高的成功概率.
结论:
- 可以为PDE学习中的数据效率建立理论保证.
- 随机线性代数和PDE理论使得可以证明高效的学习算法.
- 开发的算法在数据效率高的科学机器学习方面取得了重大进展.
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