在随机组织系统中出现的普遍远程结构
Satyam Anand1,2, Guanming Zhang3,4, Stefano Martiniani5,6,7,8
1Courant Institute of Mathematical Sciences, New York University, New York, NY, 10003, USA. sa7483@nyu.edu.
Nature communications
|January 23, 2026
概括
远程结构是从杂的粒子相互作用中产生的,在物理学和机器学习中普遍抑制密度波动. 这一发现将随机梯度下降与超均性联系起来,并且具有广泛的应用.
科学领域:
- 物理 物理学 物理
- 机器学习 机器学习
- 数学 数学 是一个数学.
背景情况:
- 从局部杂动态中自我组织是常见的,但不太了解.
- 从局部相互作用中产生远程结构是一个关键的挑战.
研究的目的:
- 在随机组织粒子系统中调查普遍的远程行为.
- 将噪音诱导的超均性与机器学习动态联系起来.
- 开发一种基于噪音的自我组织理论.
主要方法:
- 研究了三个范式系统:软物质物理模型和随机梯度下降.
- 分析了对密度波动的噪声相关效应.
- 开发了一个波动水力动力学理论.
主要成果:
- 发现了对长距离密度波动的普遍抑制.
- 噪声相关性完全控制了这种远程行为.
- 与随机梯度下降相关的新兴结构有利于平面能量景观.
结论:
- 解决了噪声引起的超均性的微观起源.
- 揭示了粒子系统和神经网络损失景观之间的平行.
- 建议在材料科学,生态学和AI算法设计中的应用.
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