张量神经网络用于高维的福克-普朗克方程
Taorui Wang1, Zheyuan Hu2, Kenji Kawaguchi2
1Department of Mathematical Sciences, Worcester Polytechnic Institute, Worcester, MA, USA.
概括
张量神经网络有效地解决了高维稳定状态的福克-普朗克方程. 这种基于物理的机器学习方法使用张量前网络或辐射基函数来获得准确和计算高效的解决方案.
科学领域:
- 计算物理 计算物理
- 机器学习 机器学习
- 数字分析 数字分析
背景情况:
- 高维稳态福克-普朗克方程在各种科学领域都至关重要.
- 在高维度中以分析或数值方式解决这些方程会带来重大的计算挑战.
- 现有的方法经常与维度的诅咒作斗争.
研究的目的:
- 开发一种高效准确的方法来解决高维稳态福克-普朗克方程.
- 在基于物理的机器学习框架中利用张量神经网络.
- 为了证明这种方法在一系列维度上的有效性.
主要方法:
- 张量神经网络的应用,包括前网络或辐射基函数的张量产物.
- 与物理信息的神经网络的集成以及用于训练的随机梯度下降.
- 对于张数辐射基础函数网络的边界域和参数约束的战略选择.
主要成果:
- 张量神经网络对于平稳状态的Fokker-Planck方程在2到10维的证明效率.
- 张量传送网络有效地利用自动区分.
- 射线基函数网络避免了高维度的计算上昂贵的自动区分,在参数约束下实现了高精度.
结论:
- 张量神经网络为高维福克-普朗克方程提供了强大而高效的计算工具.
- 基于物理的机器学习方法为统计物理学及其他领域的复杂问题提供了可扩展的解决方案.
- 这种方法在依赖福克-普朗克方程建模的领域的研究中显示出重大前景.
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