Have you considered multiplying the PDE loss by a constant factor? For example, I tried this approach in one of my projects:

model.compile("adam", lr=0.003,loss_weights=[1, 100])

The second loss term here is multiplied by a factor of 100 (in my case, this term is associated with observations). This adjustment significantly improved performance.