of identification of these games starts with the (Information Based Complexity [@Woniakowski2009]) notion that computation can only be done with partial information. For instance, since the operator is infinite dimensional, one cannot directly compute with $u\in H^1_0(\Omega)$ but only with finite-dimensional *features* of $u$. An example of such finite-dimensional features is the $m$-dimensional vector $u_m:=(\int_{\Omega}u\phi_1,\ldots,\int_{\Omega}u\phi_m)$ obtained by integrating the solution $u$ of against $m$ test/measurement functions $\phi_i \in L^2(\Omega)$. However to achieve an accurate approximation of $u$ through computation with $u_m$ one must fill the information gap between $u_m$ and $u$ (i.e. construct an interpolation operator giving $u$ as a function of $u_m$). We will, therefore, reformulate the identification of this interpolation operator as a non-cooperative (min max) game where Player I chooses the source term $g$ in an admissible set/class (e.g. the unit ball of $L^2(\Omega)$) and Player II is shown $u_m$ and must approximate $u$ from these incomplete measurements. Using the energy norm $$\label{eqenergynorm}
\|u\|_a^2:=\int_{\Omega}\nabla u^T(x) a(x)\nabla u(x)\,dx,$$ to quantify the accuracy of the recovery and calling $u^*$ Player I’s bet (on the value of $u$), the objective of Player I is to maximize the approximation error $\|u-u^*\|_{a}$, while the objective of Player II is to minimize it. 