1. A simulation method using a computing system including one or more processors, the simulation method comprising: when a function value for a nominal point (NP) of an input is a first value, running a first simulation on the input; and when the function value for the NP of the input is a second value different from the first value, running a second simulation on the input; wherein the running of the second simulation comprises: (a) setting a boundary of an input distribution for the second value as a first distribution value; (b) generating input samples within the set boundary of the input distribution; (c) obtaining a worst case point (WCP) for the input by performing machine learning on the generated input samples; and (d) repeatedly performing the steps (a) to (c) while shifting the boundary of the input distribution until the boundary of the input distribution reaches a minimum critical value.