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1. An apparatus for high-order system growth modeling, wherein the apparatus comprises: a computing device configured to: receive a plurality of system data describing a system; determine at least a current rate of growth according to at least one category of system data; generate at least a second-order model of projected growth, wherein each second-order model of the at least a second-order model corresponds to a current rate of growth of the at least a current rate of growth; receive training data, wherein the training data comprises a plurality of data entries containing a plurality of environmental factor data correlated to a plurality of user summary data; sanitize the training data using a dedicated hardware unit comprising circuitry configured to perform signal processing operations, wherein sanitizing the training data comprises: determining by the dedicated hardware unit that a training data entry has a signal to noise ratio below a threshold value indicating poor data quality; and removing the training data entry from the training data; train a machine-learning model using the sanitized training data; identify at least a decrease and at least a pattern of data, wherein: the at least a decrease includes, for each category of the at least one category of system data, a decrease in a second-order model, of the at least a second-order model, that is associated with the category, wherein identifying the decrease comprises: simulating, by the trained machine-learning model, the at least a current rate of growth by applying a comprehensive set of hypothetical inputs representative of potential actual environmental factors; comparing, by the trained machine-learning model, simulated outputs prior to any change in inputs with the simulated outputs subsequent to any change in inputs; generating, by the trained machine-learning model, a summary for a user; and the at least a pattern of data includes, for each category of the at least one category of system data, a pattern of data causally associated with the decrease in an associated second-order model of the plurality of second-order models; and configure a remote device to generate a display as a function of the decrease in each second-order model of the at least a second-order model and each pattern of data; wherein the machine-learning model further aggregates individual comparisons to associate inputs with positive or negative economic implications based on user feedback of actual environmental response; and the machine-learning model further recommends or deters the user from certain proposed actions based on learned associations.
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