从微观动力学中深入学习热力学定律
Hiroto Kuroyanagi1, Tatsuro Yuge1
1Shizuoka University, Department of Physics, Shizuoka 422-8529, Japan.
Physical review. E
|December 23, 2025
概括
深度神经网络从微观粒子数据中学习热力学规律. 网络的网络网络的网络.
科学领域:
- 物理 物理学 物理
- 热力学是一种热力学.
- 机器学习 机器学习
背景情况:
- 宏观热力学定律控制着许多粒子的系统.
- 了解微观相互作用的新兴规律是一个根本的挑战.
研究的目的:
- 为了证明深度神经网络可以从微观数据中推断出宏观的热力学定律.
- 探索机器学习在发现新出现的物理原理方面的潜力.
主要方法:
- 在亚亚巴特过程中生成气体颗粒的分子动力学模拟数据.
- 训练一个深度神经网络 (DNN) 来预测粒子快照对的时间顺序.
主要成果:
- 该DNN学到了一个顺序关系与adiabatic可访问性和热力学公理一致.
- 网络的内部代表有效地作为运作.
- DNN成功地从微观模拟中概括了热力学定律.
结论:
- 机器学习可以从潜在的微观数据中发现宏观的物理定律.
- 这种方法为数据驱动的物理学发现提供了新的途径.
- 深度学习模型可以揭示复杂系统中的新兴现象.
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