用卷积神经网络+微分方程建模瞬态燃烧过程，精度超98%。
A convolutional autoencoder and neural ODE framework for surrogate modeling of transient counterflow flames
- 用卷积自编码器提取空间相关性，压缩数据超十万倍
- 在6维连续潜空间中模拟火焰演化，误差低于2%
- 适合研究复杂燃烧的快速仿真与动态预测
提出一种新型卷积自编码器神经微分方程（CAE-NODE）框架，用于瞬态二维逆流火焰的降阶建模，将传统均匀反应系统中的AE-NODE方法拓展至空间解析流场。通过卷积层提取多维场的空间相关性，使CAE能自主构建物理一致的6维连续潜空间，将高保真2D快照（256×256网格，21个变量）压缩超过10万倍。随后训练神经微分方程（NODE）以描述非线性潜空间上的连续时间动力学，实现从初始条件向前积分，预测火焰完整时序演化。结果表明，该模型可准确捕捉点火、火焰传播及向非预混状态渐变等全过程，主要物种相对误差小于~2%。本研究首次展示了CAE-NODE在多维反应流非定常动力学代理建模中的潜力。
原文摘要 · Abstract (English)
A novel convolutional autoencoder neural ODE (CAE-NODE) framework is proposed for a reduced-order model (ROM) of transient 2D counterflow flames, as an extension of AE-NODE methods in homogeneous reactive systems to spatially resolved flows. The spatial correlations of the multidimensional fields are extracted by the convolutional layers, allowing CAE to autonomously construct a physically consistent 6D continuous latent manifold by compressing high-fidelity 2D snapshots (256x256 grid, 21 variables) by over 100,000 times. The NODE is subsequently trained to describe the continuous-time dynamics on the non-linear manifold, enabling the prediction of the full temporal evolution of the flames by integrating forward in time from an initial condition. The results demonstrate that the network can accurately capture the entire transient process, including ignition, flame propagation, and the gradual transition to a non-premixed condition, with relative errors less than ~2% for major species. This study, for the first time, highlights the potential of CAE-NODE for surrogate modeling of unsteady dynamics of multi-dimensional reacting flows.
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