POLYMAT: evaluation of the norm of the deviation between data and model for the spectrum.
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How does POLYMAT evaluate the norm of the deviation between data and model for the spectrum? Each experimental curve c is known as a list of (Xi,Yi_e), for i = 1,n. For each Xi, the model prediction Yi_p of the corresponding property is evaluated by POLYMAT. The distance d between experimental curves and model properties is evaluated as d = sum_on_all_curves_c {weight[c] * error[c]} where the error[c] on curve c is evaluated as: error[c] = (1/n) * sum_on_all_points {[log(Yi_e) - log(Yi_p)]^2}, while weight[c] is a weight for that curve, as selected by the user. During iterative process, POLYMAT identifies parameters that minimize the above distance d. |
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