  The paper proposes a deep learning method specifically dealing with the
forward and inverse problem of variable coefficient partial differential
equations -- Variable Coefficient Physical Information Neural Network
(VC-PINN). The shortcut connections (ResNet structure) introduced into the
network alleviates the "Vanishing gradient" and unifies the linear and
nonlinear coefficients. The developed method was applied to four equations
including the variable coefficient Sine-Gordon (vSG), the generalized variable
coefficient Kadomtsev-Petviashvili equation (gvKP), the variable coefficient
Korteweg-de Vries equation (vKdV), the variable coefficient Sawada-Kotera
equation (vSK). Numerical results show that VC-PINN is successful in the case
of high dimensionality, various variable coefficients (polynomials,
trigonometric functions, fractions, oscillation attenuation coefficients), and
the coexistence of multiple variable coefficients. We also conducted an
in-depth analysis of VC-PINN in a combination of theory and numerical
experiments, including four aspects, the necessity of ResNet, the relationship
between the convexity of variable coefficients and learning, anti-noise
analysis, the unity of forward and inverse problems/relationship with standard
PINN.
