Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics
By Weiqi Ji, Weilun Qiu, Zhiyu Shi, Shaowu Pan, Sili Deng. AAAI Spring Symposium: MLPS, 2020
315 citations recorded by the source corpus.
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.
Subfields: Biomedical Engineering, Mechanical Engineering, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/f94fc4d26ec2b9ec52603c76eb04492febaafeb1
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Citations in the corpus
- Characterizing possible failure modes in physics-informed neural networks (2021, background): “For example, it did not work well for stiff ordinary differential equations (ODEs) describing chemical kinetics [15], for certain heterogeneous media problems [7], or for certain fluid flow problems [10].”