How does loss functions work in PINN? [D]

I am learning Physics informed neural network (PINN). I am playing with simple 1rst/2nd 1D ODEs and I am calculating the loss functions by adding the initial condition loss and Physics loss (e.g. Total loss = lambda1 (L1) * Physics_loss (PL) + lambda2 (L2) * IC_loss (IL)). Regardless of the magnitude of the loss and lambda values, the total loss is a single numeric a value. How does the neural network model predicts if I impose higher weights (lambda) for one of the losses. For instance,

lets say, PL = 5, IC_Loss = 3, L1 = 0.6 ,L2 = 1, then total loss = 6. However, this values 6 can be achieved through several other combinations. For instance, L1 = 1 and L2 = 0.33 would result in a similar value. Given this, how the model actually learns which losses are given more weightage, which are not, and uses this information to correct its predictions?