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使用机器学习预测Mpemba效应

Felipe Amorim¹, Joey Wisely¹, Nathan Buckley¹

¹Ave Maria University, Ave Maria, Florida 34142, USA.

Physical review. E

|September 19, 2023

概括

机器学习在Ising模型中准确地预测了Mpemba效应. 神经网络甚至可以通过不发生的数据预测效应,展示超出训练数据的预测能力.

科学领域:

热力学是一种热力学.
统计力学统计力学
机器学习机器学习

背景情况:

姆佩巴效应,即温暖的水可以比冷的水更快地结,是一种反直觉的现象.
在不平衡热力学和马科维动力学中研究姆佩巴效应提供了一个理论框架.
伊辛模型作为观察马科维亚姆佩姆巴效应的相关系统.

研究的目的:

通过机器学习,研究Ising模型中马科维安Mpemba效应的可预测性.
为了比较各种机器学习算法在预测这种热力学现象中的准确性.
探索在Mpemba效应数据上训练的机器学习模型的推断能力.

主要方法:

机器学习算法的应用:决策树,神经网络,线性回归和LASSO回归.
在展示马科维动态的伊辛格模型数据上的训练和测试模型.
对模型性能的分析,包括正负准确性和推断能力.

主要成果:

机器学习方法在Ising模型中成功预测了Mpemba效应.
模型可以准确地推断到其训练范围之外的数据.
神经网络可以预测Mpemba效应,即使仅在它缺席的数据上进行训练.
神经网络在反铁磁数据 (阴性J) 上训练时,正确预测铁磁相互作用 (正J) 的Mpemba效应的缺失.

结论:

机器学习为预测复杂系统中的Mpemba效应提供了一个强大的工具.
预测能力超越了训练数据,揭示了潜在的模式.
这种方法绕过了计算密集的自向量计算的需要.

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