论文标题
部分可观测时空混沌系统的无模型预测
Kinetics Parameter Optimization via Neural Ordinary Differential Equations
论文作者
论文摘要
化学动力学机制对于理解，分析和模拟复杂燃烧现象至关重要。在这项研究中，采用神经普通微分方程（神经ode）框架来优化反应机制的动力学参数。给定实验性或高成本模拟观测值作为训练数据，所提出的算法可以最佳地恢复数据中的隐藏特征。测试了各种尺寸，类型和噪声水平的不同数据集。僵硬的罗伯逊·奥德（Robertson Ode）的经典玩具问题首先用于证明神经颂歌方法的学习能力，效率和鲁棒性。然后分别用物种的时间剖面和点火延迟时间优化了41种41种，232个反应JP-10骨骼机制和34种特性，121个反应N-甲基骨骼机制。结果表明，所提出的算法可以以足够的准确性和效率来优化僵硬的化学模型。值得注意的是，受过训练的机制不仅完全符合数据，而且还保留其物理解释性，可以在实际的湍流燃烧模拟中进一步整合和验证。
Chemical kinetics mechanisms are essential for understanding, analyzing, and simulating complex combustion phenomena. In this study, a Neural Ordinary Differential Equation (Neural ODE) framework is employed to optimize kinetics parameters of reaction mechanisms. Given experimental or high-cost simulated observations as training data, the proposed algorithm can optimally recover the hidden characteristics in the data. Different datasets of various sizes, types, and noise levels are tested. A classic toy problem of stiff Robertson ODE is first used to demonstrate the learning capability, efficiency, and robustness of the Neural ODE approach. A 41-species, 232-reactions JP-10 skeletal mechanism and a 34-species, 121-reactions n-heptane skeletal mechanism are then optimized with species' temporal profiles and ignition delay times, respectively. Results show that the proposed algorithm can optimize stiff chemical models with sufficient accuracy and efficiency. It is noted that the trained mechanism not only fits the data perfectly but also retains its physical interpretability, which can be further integrated and validated in practical turbulent combustion simulations.
