Generalized parabolic interpolation

Disclaimer. This answer is more or less a copy from material in

Quadrilateral Interpolation


Theory is specified for a rectangular square lattice, therefore isoparametric transformations are out of order here.
The above shape is known in Finite Difference circles as a five point star. Let its coordinates be given by: $$ (0) = (0,0) \quad ; \quad \begin{cases} (1) = (-1,0) \quad ; \quad (2) = (+1,0) \\ (3) = (0,-1) \quad ; \quad (4) = (0,+1)\end{cases} $$ Let function behaviour "inside" the five point star be approximated by a quadratic interpolation between the function values at the vertices or nodal points, let $T$ be such a function and use its Taylor expansion around the origin $(0)$: $$ T(x,y) = T(0) + \frac{\partial T}{\partial x}(0).x + \frac{\partial T}{\partial y}(0).y + \frac{1}{2} \frac{\partial^2 T}{\partial x^2}(0).x^2 + \frac{1}{2} \frac{\partial^2 T}{\partial y^2}(0).y^2 $$ Specify $T$ for the vertices with the basic polynomials of the five point star: $$ T_0 = T(0)\\ T_1 = T(0) - \frac{\partial T}{\partial x}(0) + \frac{1}{2} \frac{\partial^2 T}{\partial x^2}(0)\\ T_2 = T(0) + \frac{\partial T}{\partial x}(0) + \frac{1}{2} \frac{\partial^2 T}{\partial x^2}(0)\\ T_3 = T(0) - \frac{\partial T}{\partial y}(0) + \frac{1}{2} \frac{\partial^2 T}{\partial y^2}(0)\\ T_4 = T(0) + \frac{\partial T}{\partial y}(0) + \frac{1}{2} \frac{\partial^2 T}{\partial y^2}(0)\\ \quad \mbox{ F.E. } \leftarrow \mbox{ F.D. } $$ Solving these equations is not much of a problem and well-known Finite Difference schemes are recognized: $$ T(0) = T_0 \\ \frac{\partial T}{\partial x}(0) = \frac{T_2-T_1}{2}\\ \frac{\partial T}{\partial y}(0) = \frac{T_4-T_3}{2} \\ \frac{\partial^2 T}{\partial x^2}(0) = T_1-2T_0+T_2 \\ \frac{\partial^2 T}{\partial y^2}(0) = T_3-2T_0+T_4 \\ \quad \mbox{ F.D. } \leftarrow \mbox{ F.E. } $$ Finite Element shape functions may be constructed as follows: $$ T = N_0.T_0 + N_1.T_1 + N_2.T_2 + N_3.T_3 + N_4.T_4 = \\ T_0 + \frac{T_2-T_1}{2}x + \frac{T_4-T_3}{2}y + \frac{T_1-2T_0+T_2}{2}x^2 + \frac{T_3-2T_0+T_4}{2}y^2 =\\ (1-x^2-y^2)T_0 + \frac{1}{2}(-x+x^2)T_1 + \frac{1}{2}(+x+x^2)T_2 +\frac{1}{2}(-y+y^2)T_3 + \frac{1}{2}(+y+y^2)T_4 \\ \Longrightarrow \quad \begin{cases} N_0 = 1-x^2-y^2 \\ N_1 = (-x+x^2)/2\\ N_2 = (+x+x^2)/2\\ N_3 = (-y+y^2)/2\\ N_4 = (+y+y^2)/2 \end{cases} $$ So far so good concerning the generalities. Now specify for the problem at hand: $$ \frac{\partial T}{\partial x} = \frac{\partial T}{\partial x}(0)+\frac{\partial^2 T}{\partial x^2}(0).x = \frac{T_2-T_1}{2} + (T_1-2T_0+T_2).x = 0 \\ \Longrightarrow \quad x = - \frac{(T_2-T_1)/2}{T_1-2T_0+T_2} \\ \frac{\partial T}{\partial y} = \frac{\partial T}{\partial y}(0)+\frac{\partial^2 T}{\partial y^2}(0).y = \frac{T_4-T_3}{2} + (T_3-2T_0+T_4).y = 0 \\ \Longrightarrow \quad y = - \frac{(T_4-T_3)/2}{T_3-2T_0+T_4} $$ Substitute these $(x,y)$ values into either the F.D. or the F.E. formulation for $T(x,y)$ and you're done. The above can easily be generalized for multiple dimensions. As has been done in:

Linear isoparametrics with dual finite elements

For example a generalization to three dimensions, being the well-known seven point star: $$ (0) = (0,0,0) \quad ; \quad \begin{cases} (1) = (-1,0,0) \quad ; \quad (2) = (+1,0,0) \\ (3) = (0,-1,0) \quad ; \quad (4) = (0,+1,0) \\ (5) = (0,0,-1) \quad ; \quad (6) = (0,0,+1)\end{cases} $$ With accompanying interpolation: $$ T(x,y,z) = T_0 + \frac{T_2-T_1}{2}x + \frac{T_4-T_3}{2}y + \frac{T_6-T_5}{2}z\\ + \frac{T_1-2T_0+T_2}{2}x^2 + \frac{T_3-2T_0+T_4}{2}y^2 + \frac{T_5-2T_0+T_6}{2}z^2 $$ The place $\,(x,y,z)\,$ where the function $\,T(x,y,z)\,$ has its maximum is found by: $$ \frac{\partial T}{\partial x} = 0 \quad \Longrightarrow \quad x = - \frac{(T_2-T_1)/2}{T_1-2T_0+T_2} \\ \frac{\partial T}{\partial y} = 0 \quad \Longrightarrow \quad y = - \frac{(T_4-T_3)/2}{T_3-2T_0+T_4} \\ \frac{\partial T}{\partial z} = 0 \quad \Longrightarrow \quad z = - \frac{(T_6-T_5)/2}{T_5-2T_0+T_6} $$ At last substitute these $(x,y,z)$ values into the interpolation formula for $T(x,y,z)$ . Analogous procedure for $d$ dimensional space requires a stencil with $2d+1$ nodes, e.g. $13$ nodes for $d=6$.

UPDATE. A picture says more than a thousand words:

It is seen that the five node stars in the grid have double chemical bonds, so to speak. This is the way all data points are actually available. However, there is some ambiguity involved in assigning the function values - already present in 1D. Which can be resolved by assigning a certain domain - shaded region around the central node $(0)$ - to each of the five node stars.