标题： KiNet：化学动力学的深度神经网络表示 显示英文标题

标题： KiNet: A Deep Neural Network Representation of Chemical Kinetics

摘要： 深度学习是一种从实验数据中自动开发动力学模型的潜在方法。 我们提出了一种KiNet的深度神经网络模型来表示化学动力学。 KiNet根据当前组成状态预测固定时间步长后的状态演变。 通过多次递归应用KiNet模型，可以高效地获得状态的长期演变及其对模型参数的梯度。 为了解决高维组成空间和长期预测中的误差累积问题，KiNet的架构结合了残差网络模型（ResNet），并且训练采用通过时间反向传播（BPTT）方法来最小化多步预测误差。 此外，提出了一种高效计算点火延迟时间（IDT）对KiNet模型参数梯度的方法，以训练KiNet与文献中的丰富IDT数据库相匹配，这可以解决时间分辨物种测量的稀缺性。 KiNet首先在氢气/空气混合物自燃过程中的模拟物种轮廓上进行训练和比较。 获得的KiNet模型可以准确预测各种初始条件下的自燃过程，这些初始条件涵盖了广泛的压力、温度和当量比。 然后，我们表明IDT对KiNet模型参数的梯度与点火点温度的梯度平行。 这种相关性使得可以通过反向传播高效计算IDT的梯度，并被证明是针对IDT微调KiNet的可行方法。 这些演示将开启自主构建数据驱动动力学模型的可能性。 最后，训练好的KiNet可能被应用于湍流燃烧模拟中的动力学模型简化和化学加速。 显示英文摘要

摘要： Deep learning is a potential approach to automatically develop kinetic models from experimental data. We propose a deep neural network model of KiNet to represent chemical kinetics. KiNet takes the current composition states and predicts the evolution of the states after a fixed time step. The long-period evolution of the states and their gradients to model parameters can be efficiently obtained by recursively applying the KiNet model multiple times. To address the challenges of the high-dimensional composition space and error accumulation in long-period prediction, the architecture of KiNet incorporates the residual network model (ResNet), and the training employs backpropagation through time (BPTT) approach to minimize multi-step prediction error. In addition, an approach for efficiently computing the gradient of the ignition delay time (IDT) to KiNet model parameters is proposed to train the KiNet against the rich database of IDT from literature, which could address the scarcity of time-resolved species measurements. The KiNet is first trained and compared with the simulated species profiles during the auto-ignition of H2/air mixtures. The obtained KiNet model can accurately predict the auto-ignition processes for various initial conditions that cover a wide range of pressures, temperatures, and equivalence ratios. Then, we show that the gradient of IDT to KiNet model parameters is parallel to the gradient of the temperature at the ignition point. This correlation enables efficient computation of the gradient of IDT via backpropagation and is demonstrated as a feasible approach for fine-tuning the KiNet against IDT. These demonstrations shall open up the possibility of building data-driven kinetic models autonomously. Finally, the trained KiNet could be potentially applied to kinetic model reduction and chemistry acceleration in turbulent combustion simulations.