在拉格朗的动态系统中学习的平衡传播
1Université Libre de Bruxelles, Laboratoire d'Information Quantique CP224, (ULB), Av. F. D. Roosevelt 50, 1050 Bruxelles, Belgium.
Physical review. E
|October 21, 2025
概括
我们为动态系统引入了一种新的训练方法,使用平衡传播和动作极端化. 这种方法有效地更新了具有固定或周期性边界条件的系统的参数,避免了复杂的反向传播.
科学领域:
- 计算物理学的计算物理.
- 机器学习 机器学习
- 动态系统是动态系统.
背景情况:
- 均衡传播 (EP) 对于训练基于能源的模型是有效的.
- 训练动态系统通常需要通过时间进行计算密集的反向传播.
- 拉格朗日力学为描述系统动态提供了一个框架.
研究的目的:
- 为由拉格朗日力学控制的动态系统开发一种新的训练方法.
- 为了将平衡传播扩展到动态轨迹.
- 为了实现有效的参数更新,而无需明确的反向传播.
主要方法:
- 利用动作极端化的原理来适应平衡传播.
- 向目标推向轨迹,并测量并联变量响应.
- 将该方法应用于具有周期边界条件和固定的初始/最终状态的系统.
主要成果:
- 拟议的方法为拉格朗的动态系统提供了高效的参数更新.
- 对于周期边界条件,它恢复了量子平衡传播的半经典极限.
- 这种方法适用于散射系统.
结论:
- 本书介绍了一种有效且广泛适用的动态系统培训方法.
- 该技术为特定系统类型提供了传统反向传播的替代方案.
- 它跨越了拉格朗日力学,平衡传播和量子力学的概念.
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