Can I preserve the variables during the trainning?

[Lastilla]

Just like the example in the invers about N-S Function, I set 2 variables:
C1 = dde.Variable(0.0)
C2 = dde.Variable(0.0)

And my pde:
def Navier_Stokes_Equation(x, y):
u = y[:, 0:1]
v = y[:, 1:2]
p = y[:, 2:3]
du_x = dde.grad.jacobian(y, x, i=0, j=0)
du_y = dde.grad.jacobian(y, x, i=0, j=1)
dv_x = dde.grad.jacobian(y, x, i=1, j=0)
dv_y = dde.grad.jacobian(y, x, i=1, j=1)
dp_x = dde.grad.jacobian(y, x, i=2, j=0)
dp_y = dde.grad.jacobian(y, x, i=2, j=1)
du_xx = dde.grad.hessian(y, x, component=0, i=0, j=0)
du_yy = dde.grad.hessian(y, x, component=0, i=1, j=1)
dv_xx = dde.grad.hessian(y, x, component=1, i=0, j=0)
dv_yy = dde.grad.hessian(y, x, component=1, i=1, j=1)
continuity = du_x + dv_y
x_momentum = C1 * (u * du_x + v * du_y) + dp_x - C2 * (du_xx + du_yy)
y_momentum = C1 * (u * dv_x + v * dv_y) + dp_y - C2 * (dv_xx + dv_yy)
return [continuity, x_momentum, y_momentum]

I want to keep the variables during the trainning.

[lululxvi]

Don't understand "keep the variables during the trainning"