The Lid driven cavity flow example does not use a pressure boundary condition, but for ordinary CFD, a boundary condition of zero pressure gradient is imposed on the wall surface.
So I used GradNormal as shown in the attached file, and added a boundary condition where “normal_gradient_p” is zero on the wall surface, but it does not seem to be learning well. Could you give me any ideas as to the cause or what I should fix?
ldc_2d.py (3.7 KB)
Hi @user106225
With physics-informed learning, similar methods to numerical solvers can be used but do not always work the best. The optimization of a solver and a neural network are very different, thus sometimes different strategies need to be used for training a AI surrogate. For all practical purposes, its largely comes down to empirical testing. Each PDE is different and may require different strategies for getting good convergence.
Does this mean a pressure gradient boundary won’t work? No, I’m sure there’s a way to weight the losses or adjust the learning appropriately to get something the learns better. But that’s up to the user to figure out. I would suggest looking through the different examples we have as well as the literature in the field for guidance based on methods other’s have found successful. We also have a recommended practices section in our user guide with some typical strategies we found to help.