Solution of conservative-form transport equations with physics-informed neural network

Abstract

Physics-informed neural network (PINN) has recently gained significant attention in the field of scientific computing. This paper proposes a PINN framework for solving conservative-form transport equations (PINNforCTE). The PINNforCTE features an accelerated back-propagation in the neural network (NN) training due to low-order derivatives in the governing equations. We demonstrate the application of PINNforCTE in simulating a classical flow and heat transfer problem, i.e., the lid-driven cavity flow and heat transfer under three types of boundary conditions, and the simulation results are compared with those obtained from the traditional PINN and the finite element CFD method. The comparisons indicate that PINNforCTE can significantly accelerate the NN training and potentially shorten the solution time. For all cases considered, the training time of PINNforCTE is reduced by approximately ∼50% compared with the conventional PINN. Furthermore, PINNforCTE can accelerate the Adam optimization algorithm (L-BFGS-B optimization algorithm) by ∼100% (∼80%) compared to the traditional PINN. Additionally, PINNforCTE shows greater convenience in applying the Neumann and Robin boundary conditions directly and higher calculation accuracy than the conventional PINN. Finally, the ability of PINNforCTE to solve the inverse problems is confirmed with an example of 2D steady lid-driven cavity flow and heat transfer. PINNforCTE is an efficient NN solver for conservative-form transport equations (CTEs) that are ubiquitous in science and engineering.