Weak form for 'Inverse Laplace' Winslow equations

Hello,

As part of an investigation into adaptive and moving mesh techniques, I have been exploring the possible use of fenics to solve Winslow’s equations for (x,y) on a domain \xi\in[0, 1], \eta \in [0, 1]:

\alpha x_{\xi\xi} - 2\beta x_{\xi\eta} + \gamma x_{\eta\eta} = -[D_{\xi}(x,y)y_{\eta} - D_{\eta}(x,y)y_{\xi}]\dfrac{J}{D(x,y)}
\alpha y_{\xi\xi} - 2\beta y_{\xi\eta} + \gamma y_{\eta\eta} = -[D_{\xi}(x,y)x_{\eta} - D_{\eta}(x,y)x_{\xi}]\dfrac{J}{D(x,y)}

where

\alpha = x_{\eta}^2 + y_{\eta}^2
\beta = x_{\xi}x_{\eta} + y_{\xi}y_{\eta}
\gamma = x_{\xi}^2 + y_{\xi}^2

and J is the Jacobian determinant J = x_{\xi}y_{\eta} - x_{\eta}y_{\xi} , and where u_{v} = \dfrac{\partial u}{\partial v} and u_{vw} = \dfrac{\partial^2 u}{\partial v \partial w}.

The above are the inverse to the laplace equations

\nabla\cdot(D(x,y)\nabla\xi) = 0 and \nabla\cdot(D(x,y)\nabla \eta) = 0

giving a mapping between (x,y) and (\xi, \eta).

While the original laplace equations for \xi and \eta are trivially easy to implement using fenics, I am honestly struggling to obtain a weak form for this set of ‘inverse laplace’ equations. Particularly with dealing with the second-order derivatives when they are also multiplied by the first-order terms \alpha, \beta, \gamma. Most papers that use this method solves it using finite differences instead.

If anybody could provide some guidance that would be greatly appreciated!

Thanks,
Tim

As an update for anyone curious, I managed to effectively solve this set of equations by manually inverting the mapping from just solving the laplace equations (second set in the post). Just took a fair bit of DOF and node wrangling. Works fairly well though.

I would still be interested to see what the correct strategy is to solve the full inverse equations though!