Calibrating chemical kinetics in a reaction-diffusion system is challenging because of complex dynamics governed by tightly coupled chemistry and transport, while experimental observations are often sparse and noisy. We propose a physics consistent diffusion-chemistry coupled neural ordinary differential equation (Diff-Chem Neural ODE) that embeds Arrhenius-structured reaction neurons into a fully differentiable streamline formulation and explicitly accounts for diffusion coupling. This design enables direct gradient-based analysis of kinetic parameters without sampling-based pretraining. We validate this method on burner-stabilized flat and stagnation reacting flows using mechanisms spanning different stiffness ranges. The proposed method reproduces species profiles with near-reference accuracy, whereas a pure chemistry Neural ODE that neglects diffusion coupling may misplace ignition and generate an incorrect thin reaction zone. Diff-Chem Neural ODE is more robust than pure chemistry Neural ODE and provides substantial speedups for gradient evaluation compared with fully discretized computations. In kinetics refinement, optimizing only a limited set of "primal" species reduces the loss by over 98% and simultaneously recovers unobserved variables, demonstrating physically consistent global control. Finally, tests with 1-20% noise in the objective show stable convergence without local overfitting, supporting its applicability under noisy measurements.

翻译：在反应-扩散系统中标定化学动力学是一项具有挑战性的工作，因为其中涉及由化学与输运过程紧密耦合所主导的复杂动力学，而实验观测往往稀疏且包含噪声。我们提出一种物理一致的扩散-化学耦合神经常微分方程（Diff-Chem Neural ODE），该方法将阿伦尼乌斯结构化的反应神经元嵌入到完全可微的流线型框架中，并显式地考虑了扩散耦合效应。这种设计无需基于采样的预训练即可直接对动力学参数进行基于梯度的分析。我们在包含不同刚性范围的机理下，对燃烧器稳定的平面和滞止反应流进行了该方法验证。结果表明，所提方法能以接近参考解的精度重现组分剖面，而忽略扩散耦合的纯化学神经ODE则可能错误地定位着火点并生成错误的薄反应区。Diff-Chem Neural ODE相比纯化学神经ODE具有更强的鲁棒性，并且在梯度评估方面相较于完全离散化计算提供了显著的加速。在动力学参数优化中，仅优化有限的一组“原始”组分即可使损失函数降低超过98%，并同时恢复未观测到的变量，展现了物理一致的全局控制能力。最后，在目标函数包含1-20%噪声的测试中，该方法表现出稳定收敛且无局部过拟合，证明了其在含噪测量条件下的适用性。