Simulation system and method thereof and computing system including the simulation system

A simulation method using a computing system including one or more processors, the simulation method comprising:

• (a) setting a boundary of an input distribution as a first distribution value;
• (b) generating input samples within the set boundary of the input distribution;
• (c) obtaining function values for the respective generated input samples;
• (d) obtaining a boundary plane by performing machine learning on the respective generated input samples;
• (e) determining a worst case point (WCP) for the input on the boundary plane;
• and
• (f) repeatedly performing the steps (a) to (e) while shifting the boundary of the input distribution until the boundary of the input distribution reaches a minimum critical value.

A simulation system comprising:

• a main simulation module running a simulation operation based on a center of a provided distribution;
• a determining module providing a zero point (ZP) for an input to the main simulation module when a function value for a nominal point (NP) of the input is a first value, and running a second simulation on the input when the function value for the NP of the input is a second value different from the first value;
• and
• a worst case point (WCP) determining module obtaining a WCP for the input by repeatedly performing generating input samples within a set boundary of the input distribution while shifting a boundary of an input distribution for the second value, and providing the obtained WCP to the main simulation module.

A computing system comprising:

• one or more processors;
• and
• a storage unit in which program codes for performing a simulation for an input using the processors are stored,
• wherein when a function value for a nominal point (NP) of the input is a first value, a first simulation for the input is run, and when the function value for the NP of the input is a second value different from the first value, a second simulation for the input is run, the running of the second simulation comprising:
• (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.

A simulation method for a computing system including one or more processors, the simulation method comprising:

• performing a main simulation as one of a first simulation and a second simulation based on a provided input distribution;
• in a case where a function value for a nominal point of the input distribution is a first value, providing a zero point (ZP) for the input distribution to the main simulation, and the main simulation performing the first simulation in response thereto;
• and
• in a case where the function value for the nominal point of the input distribution is a second value that is different than the first value, performing a worst case point (WCP) determination for the input distribution by repeatedly performing generating input samples within a set boundary of the input distribution while shifting a boundary of the input distribution, providing the determined worst case point (WCP) value to the main simulation, and the main simulation performing the second simulation in response thereto.