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.

本研究对使用物理学知识提供指导的神经网络（PINN）在处理化学动力学问题中的表现进行了研究，发现针对刚性问题的化学动力学系统，普通的 PINN 存在失败的可能性，因此本研究开发了使用准稳态假设的 Stiff-PINN 方法以解决化学动力学问题中的刚性问题。