论文标题
僵硬的细菌：僵硬的化学动力学的物理信息神经网络
Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics
论文作者
论文摘要
最近开发的物理信息神经网络（PINN）通过将物理定律编码到神经网络的损失功能中，在许多科学和工程学科中取得了成功，因此该网络不仅符合测量，初始和边界条件，而且还满足了管理方程。这项工作首先研究了Pinn在解决僵硬的普通微分方程（ODES）方程解决僵硬的化学动力学问题方面的性能。结果阐明了在硬ode系统中使用PINN的挑战。因此，我们采用准稳态施用（QSSA）来降低ode系统的刚度，然后可以成功地应用于转换后的非/轻度-STIFT系统。因此，结果表明，刚度可能是在研究的刚性化学动力学系统中常规PINN失败的主要原因。开发的僵硬的Pinn方法利用QSSA来使PINN求解僵硬的化学动力学，应为将PINN应用于涉及僵硬动态的各种反应扩散系统施用。
Recently developed physics-informed neural network (PINN) has achieved success in many science and engineering disciplines by encoding physics laws into the loss functions of the neural network, such that the network not only conforms to the measurements, initial and boundary conditions but also satisfies the governing equations. This work first investigates the performance of PINN in solving stiff chemical kinetic problems with governing equations of stiff ordinary differential equations (ODEs). The results elucidate the challenges of utilizing PINN in stiff ODE systems. Consequently, we employ Quasi-Steady-State-Assumptions (QSSA) to reduce the stiffness of the ODE systems, and the PINN then can be successfully applied to the converted non/mild-stiff systems. Therefore, the results suggest that stiffness could be the major reason for the failure of the regular PINN in the studied stiff chemical kinetic systems. The developed Stiff-PINN approach that utilizes QSSA to enable PINN to solve stiff chemical kinetics shall open the possibility of applying PINN to various reaction-diffusion systems involving stiff dynamics.
