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Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics
Name
2011.04520.pdf
Description
Accepted version
Size
1.26 MB
Format
Adobe PDF
Checksum (MD5)
5a58f2060d8c02649ea16dfba938a1d7
Author(s)
Ji, Weiqi
Qiu, Weilun
Shi, Zhiyu
Pan, Shaowu
Deng, Sili
Date Issued
2021
Journal
Journal of Physical Chemistry A
Publisher
American Chemical Society (ACS)
Citation
Ji, Weiqi, Qiu, Weilun, Shi, Zhiyu, Pan, Shaowu and Deng, Sili. 2021. "Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics." Journal of Physical Chemistry A, 125 (36).
Version
Author's final manuscript
Abstract
The 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 and initial and boundary conditions but also satisfies the governing equations. This work first investigates the performance of the PINN in solving stiff chemical kinetic problems with governing equations of stiff ordinary differential equations (ODEs). The results elucidate the challenges of utilizing the PINN in stiff ODE systems. Consequently, we employ quasi-steady-state assumption (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 the PINN to solve stiff chemical kinetics shall open the possibility of applying the PINN to various reaction-diffusion systems involving stiff dynamics.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1021/ACS.JPCA.1C05102
