Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics   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.

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The categories of this paper are: hep-th
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The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
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The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas