神经网络对多相状态方程的表示
George A Kevrekidis1,2, Daniel A Serino3, M Alexander R Kaltenborn3
1Los Alamos National Laboratory, Los Alamos, NM, USA. gkevrek1@jh.edu.
Scientific reports
|December 5, 2024
概括
开发了两种新的深度学习方法来创建准确的状态方程 (EoS),这些方程服从热力学定律和模型相位过渡,从而推进科学建模.
科学领域:
- 热力学是一种热力学.
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 状态方程 (EoS) 对各种科学领域的热力学变量建模至关重要.
- 现有的分析模型往往难以同时满足热力学定律,结合相变,并保持多尺度的准确性.
- 开发全面的EdS模型,平衡准确性和效率仍然是一个重大挑战.
研究的目的:
- 为构建状态方程提出两种深度学习方法.
- 确保这些模型遵守热力学定律,并准确地表示相位过渡.
- 提供灵活的EoS建模解决方案,可独立使用或增强现有模型.
主要方法:
- 开发了一种基于学习热力学潜力 (生成函数) 的深度学习方法.
- 引入了结构保存的,用于EoS建模的简单的神经网络.
- 这两种方法都旨在有效处理阶段过渡地区.
主要成果:
- 提出的深度学习方法可以证明满足了基本的热力学定律和模型阶段过渡.
- 证明了这些方法能够从头开始学习完整的EoS模型的能力.
- 展示了它们在完善现有EdS模型以更好地匹配实验数据中的实用性.
结论:
- 深度学习为开发准确和多功能状态方程提供了一个强大的框架.
- 这些新的方法为克服传统分析式EdS模型的局限性提供了一条途径.
- 提出的方法提高了热力学建模在科学和工程中的可靠性和适用性.
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