Predicting high-fidelity multiphysics data from low-fidelity fluid flow and transport solvers using physics-informed neural networks

High-fidelity models of multiphysics fluid flow processes are often computationally expensive. On the other hand, less accurate low-fidelity models could be efficiently executed to provide an approximation to the solution. Multi-fidelity approaches combine high-fidelity and low-fidelity data and/or models to obtain a desirable balance between computational efficiency and accuracy. In this manuscript, we propose a multi-fidelity approach where we combine data generated by a low-fidelity computational fluid dynamics (CFD) solution strategy (solver settings and resolution) and data-free physics-informed neural networks (PINN) to obtain improved accuracy. Specifically, transfer learning based on low-fidelity CFD data is used to initialize PINN. Subsequently, PINN with this physics-guided initialization is used to obtain the final results without any high-fidelity training data. The accuracy of the final results relies on the governing equations encoded in PINN together with the low-fidelity CFD data initialization. To investigate the accuracy of this approach, several partial differential equations were solved to predict velocity and temperature in different fluid flow, heat transfer, and porous media transport problems. Comparison with reference high-fidelity CFD data revealed that the proposed approach not only significantly improves the accuracy of low-fidelity CFD data but also improves the convergence speed and accuracy of PINN.