用于燃烧化学建模和加速的ChemKANs
Benjamin C Koenig1, Suyong Kim1, Sili Deng1
1Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA. silideng@mit.edu.
Physical chemistry chemical physics : PCCP
|August 1, 2025
概括
一个新的神经网络ChemKANs有效地推断出燃烧的化学动力模型. 这个框架加速了模拟,并证明了对噪音数据的弹性,为燃烧化学应用提供了强大的工具.
科学领域:
- 燃烧化学 燃烧化学是什么
- 化学动力学 化学动力学
- 机器学习 机器学习
背景情况:
- 化学动力模型推断和在燃烧中的应用面临着由于大型ODE系统和不同的时间尺度的挑战.
- 机器学习 (ML) 提供了精简这些模型的潜力,但非线性,刚性和噪音数据带来了困难.
研究的目的:
- 介绍ChemKANs,一种用于燃烧化学模型推断和模拟加速的新型神经网络框架.
- 用化学动力学和热力学原理增强通用Kolmogorov-Arnold网络普通微分方程 (KAN-ODEs).
主要方法:
- 通过增强KAN-ODEs的化学特定信息流的知识来开发ChemKANs.
- 基于基准的ChemKAN用于对稀疏,杂的数据和大参数化的模型推断稳定性.
- 对燃烧化学的模拟加速进行评估的ChemKANs.
主要成果:
- 与基准相比,ChemKANs表现出强烈的诱导偏差,简化训练和更高的准确性.
- 没有过度装配或降解,数据噪声高达15%,参数化很大,显示了弹性.
- 一个参数薄的ChemKAN (344个参数) 准确地表示了燃烧,实现了2倍的加速.
结论:
- 化学KANs为燃烧中的化学动力模型推断提供了一种强大,富有表现力和高效的方法.
- 该框架显示了加速燃烧物理和化学动力学模拟的巨大潜力.
- 化学KAN能够抵御常见的深度学习失败模式,使其可靠用于复杂的科学应用.
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