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Local and Global Mass Balance of Physics-Informed Neural Network (PINN)

Presentation Date
Monday, December 12, 2022 at 3:05pm - Monday, December 12, 2022 at 3:15pm
Location
McCormick Place - E451a
Authors

Author

Abstract

Physics-informed neural network (PINN) is an alternative technique to solve partial differential equations (PDEs). The PINN approach can solve any dimension of ordinary and/or partial differential equations through automatic differentiation incorporating initial and boundary conditions. PINN loosely satisfies the local mass balance through a loss function; however, there is no comprehensive study on the deviation of balance laws. In this study, we developed a PINN framework to solve PDEs for steady-state groundwater flow in both homogeneous and heterogeneous porous media. Next, we investigated the local and global mass balances of the PINN solution. Finally, we discussed the local and global mass balances of PINN through numerical h-convergence, iteration, and collocation points.

Funding Program Area(s)