An integral part of deterring nuclear terrorism is the swift attribution of any event to a particular state or organization. By quickly being able to identify the responsible party after a nuclear event, appropriate people may be held accountable for their actions. Currently, there is a system in place to determine the origin of nuclear devices and materials from post-event data; however, the system requires significant time to produce an answer within acceptable error margins. Described here is a deterministic approach derived from first principles to solve the inverse problem. The derivation starts with the basic change rate equation and ends in relationships for important nuclear concentrations and device yield. This results in a computationally efficient and timely method for producing an estimate of the material attributes. This estimate can then be used as a starting point for other more detailed methods and reduce the overall computation time of the post-event forensics. This work focused on a specific type of nuclear event: a plutonium improvised nuclear device (IND) explosion. From post-event isotopic ratios, this method determines the device’s pre-event isotopic concentrations of special nuclear material. From the original isotopic concentrations, the field of possible origins for the nuclear material is narrowed. In this scenario, knowing where the nuclear material did not originate is as important as knowing where it did. The derived methodology was tested using several cases of interest including simplified and realistic cases. For the simplistic cases, only two isotopes comprised the material being fissioned. In the realistic cases, both Weapons Grade and Reactor Grade plutonium were used to cover the spectrum of possible fissile material to be used by terrorists. The methodology performed very well over the desired energy range. Errors were under two percent from the expected values for all yields under 50 kT. In the realistic cases, competing reactions caused an increase in error; however, these stayed under five percent. As expected, with an increased yield, the error continued to rise, but these errors increased linearly. A sensitivity analysis was performed on the methodology to determine the impact of uncertainty in various physical constants. The result was that the inverse methodology is not overly sensitive to perturbations in these constants.