微視的ダイナミクスから熱力学法則を深層学習する
Hiroto Kuroyanagi1, Tatsuro Yuge1
1Shizuoka University, Department of Physics, Shizuoka 422-8529, Japan.
Physical review. E
|December 23, 2025
まとめ
深層ニューラルネットワークは微視的粒子データから熱力学法則を学習する。ネットワークは
科学分野:
- 物理学
- 熱力学
- 機械学習
背景:
- 巨視的熱力学法則は多数の粒子を持つ系を支配する。
- 微視的相互作用から創発的な法則を理解することは基本的な課題である。
研究 の 目的:
- 深層ニューラルネットワークが微視的データから巨視的熱力学法則を推論できることを実証すること。
- 創発的な物理原理の発見における機械学習の可能性を探求すること。
主な方法:
- 断熱過程におけるガス粒子の分子動力学シミュレーションデータを生成すること。
- 粒子スナップショットペアの時間的順序を予測するために深層ニューラルネットワーク(DNN)をトレーニングすること。
主要な成果:
- DNNは断熱到達可能性と熱力学公理に整合する順序関係を学習した。
- ネットワークの内部表現は効果的にエントロピーとして機能した。
- DNNは微視的シミュレーションから熱力学法則を一般化することに成功した。
結論:
- 機械学習は、根底にある微視的データから巨視的物理法則を発見できる。
- このアプローチは、物理学におけるデータ駆動型発見の新しい道を提供する。
- 深層学習モデルは、複雑なシステムにおける創発現象を明らかにすることができる。
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