You could rearrange J= f(J,V) to be J - f(J,V)=0. The Y input to the Nonlinear Curve Fit.vi becomes an array of zeros, with length equal to the number of J (or V) values. Concatenate J and V data arrays, and pass as the X input to the Nonlinear Curve Fit.vi. In your model function, split the X input back to J and V, and evaluate J - f(J,V) for each (j,v).
The other way to look at this is to take J (or V)as the "independent" or given value, and use a zero finder to solve for J - f(J,V)=0 for V (or J). In this case J becomes the X input, and V becomes the Y input to Nonlinear Curve Fit.vi. This will be fairly expensive, and you may encounter some combination of parameters and values that will not admit a solution of J - f(J,V)=0.
-Jim
