Experience report of physics-informed neural networks in fluid simulations: pitfalls and frustration
Pi-Yueh Chuang
Lorena A. Barba
Though PINNs (physics-informed neural networks) are now deemed as a complement to traditional CFD (computational fluid dynamics) solvers rather than a replacement, their ability to solve the Navier-Stokes equations without given data is still of great interest.
This report presents our not-so-successful experiments of solving the Navier-Stokes equations with PINN as a replacement to traditional solvers.
We aim to, with our experiments, prepare readers for the challenges they may face if they are interested in data-free PINN.
In this work, we used two standard flow problems: 2D Taylor-Green vortex at and 2D cylinder flow at .
The PINN method solved the 2D Taylor-Green vortex problem with acceptable results, and we used this flow as an accuracy and performance benchmark.
About 32 hours of training were required for the PINN method's accuracy to match the accuracy of a finite-difference simulation, which took less than 20 seconds.
The 2D cylinder flow, on the other hand, did not produce a physical solution.
The PINN method behaved like a steady-flow solver and did not capture the vortex shedding phenomenon.
By sharing our experience, we would like to emphasize that the PINN method is still a work-in-progress, especially in terms of solving flow problems without any given data.
More work is needed to make PINN feasible for real-world problems in such applications.
(Reproducibility package: pi\_yueh\_chuang\_2022\_6592457.)
computational fluid dynamics, deep learning, physics-informed neural network
DOI10.25080/majora-212e5952-005