一种基于物理学的神经网络 (PINN) 方法,用于在保守扰乱的线性平衡系统中的过度平衡动力学
Abhishek Dutta1, Bitan Mukherjee2, Sk Aftab Hosen2
1Department of Chemical Engineering, Izmir Institute of Technology, Izmir 35430, Turkey.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
基于物理学的神经网络 (PINNs) 准确地模拟化学反应动态,在没有广泛的数据的情况下捕获暂时的超平衡度极端. 这种方法可以确保有效地满足物理保护法则.
科学领域:
- 化学动力学 化学动力学
- 计算化学是一种计算化学.
- 科学中的人工智能.
背景情况:
- 保守性扰乱平衡 (CPE) 实验显示过渡性度极端超过稳定状态值.
- 在化学反应网络中模拟这些超平衡动态在计算上具有挑战性,通常需要广泛的时间序列数据.
研究的目的:
- 引入物理信息神经网络 (PINN) 框架,用于模拟线性化学反应网络中的超平衡动态.
- 为了证明PINN能够在没有大量时间序列数据的情况下准确地捕获短暂的度极端.
主要方法:
- 开发了一种PINN框架,将反应动力学,稳定计不变量和平衡约束纳入损失函数.
- 确保PINN解决方案严格遵守物理保护法.
- 将PINN应用于三种和四种可逆反应机制 (非循环和循环).
主要成果:
- 替代PINN准确地复制了传统ODE集成的结果.
- 该模型成功地捕获了早期特征的极端度 (最大/最小) 和随后的平衡放松.
- 在预测极端的时间和大小方面取得了很高的准确性,同时保持了总质量.
结论:
- 基于物理学的方法能够准确地模拟超平衡动力学,使用最小的数据.
- PINNs提供了一个参数高效和物理约束的方法来建模复杂的化学系统.
- 这个框架展示了人工智能在推进计算化学和反应动态方面的潜力.
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