Apparatus and method for determining system model comparisons

A method for determining system model comparison, wherein the method comprises:

• receiving, by at least a processor, a first plurality of system data representing a first state of a system, wherein the first plurality of system data comprises first operational and performance metrics;
• receiving, by at least the processor, a second plurality of system data representing a second state of the system, wherein the second plurality of system data comprises second operational and performance metrics;
• generating, by the at least a processor, a first model of the system according to the first plurality of system data;
• generating, by the at least a processor, a second model of the system, using the second plurality of system data;
• outputting, by the at least a processor, a first model output using the first model of the system and the second plurality of system data;
• modifying, by the at least a processor, the second plurality of system data using at least a perturbation function;
• outputting, by the at least a processor, a second model output using the second model of the system and the modified second plurality of system data applied in the second model;
• training, by the at least a processor, a machine learning model, wherein training the machine learning model comprises:
• receiving a training dataset comprising outputs from at least the first model correlated to corresponding evaluations of the at least the first model;
• adjusting a machine learning model using the training dataset and a loss function;
• comparing new outputs of the adjusted machine learning model to the training dataset;
• processing the new outputs of the adjusted machine learning model with the outputs of the at least the first model as a function of at least a learned pattern of the adjusted machine learning model;
• and
• generating a score for the outputs of the at least the first model using the adjusted machine learning model, wherein the score for the outputs of the at least the first model indicates a quality value of the outputs of the at least the first model, wherein the quality value is compared against a quality benchmark;
• comparing, by the at least a processor, the first model output to the second model output using the trained machine learning model and the generated score;
• generating, by the at least a processor, a state change output corresponding to the system utilizing the trained machine learning model and the generated score, wherein the state change output comprises at least a proposed adjustment to a plurality of parameters of the system mirroring the perturbation function applied in the second model;
• and
• presenting, by the at least a processor, the proposed adjustment as a graphical representation on a user interface including interactive elements by activating event handlers of the user interface to refine system performance of the system based on the proposed adjustment.