Abstract: Provided is a computing system for implementing a system model using big data machine learning, which is intended to build a hypothetical model, calculate verified parameter values by performing machine learning on big data acquired from an actual system, and apply the verified parameter values to the hypothetical model. A system modeling method of completing a simulation model for a target system by causing a hypothetical model defined by acquiring knowledge about the target system to perform machine learning on big data acquired by running and observing the target system includes defining a hypothetical model for a target system by finding acquirable information related to the target system.

Abstract: Disclosed herein is a computing system for implementing and operating a system model describing a target system, the computer system comprises a processor configured to select first data as input data of a first sub-module, based on structural information, from among new input data for the target system, provide second data as input data of a second sub-module, based on the structural information, wherein the second sub-module is defined to receive output data of the first sub-module as input data thereof by the structural information, control the second sub-model to infer a behavior of the target system based on the second data, and provide third data based on output data of the second sub-module as an output of the system model describing the behavior of the target system.