System and method for facilitating prediction data for device based on synthetic data with uncertainties

A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for facilitating a training system for a device, the method comprising:

• determining a system model for the device that can be based on empirical data of the device, wherein the empirical data is obtained based on experiments performed on the device;
• generating, from the system model, synthetic data that represents behavior of the device under a failure;
• determining uncertainty associated with the synthetic data;
• determining a set of prediction parameters from the uncertainty using an uncertainty quantification model;
• generating training data from the synthetic data based on the set of prediction parameters;
• and
• learning a set of learned parameters associated with the device by using a machine-learning-based classifier or a regression model on the training data.

A computer system;

• comprising:
• a storage device;
• a processor;
• and
• a non-transitory computer-readable storage medium storing instructions, which when executed by the processor cause the processor to perform a method for facilitating a training system for a device, the method comprising:
• determining a system model for the device that can be based on empirical data of the device, wherein the empirical data is obtained based on experiments performed on the device;
• generating, from the system model, synthetic data that represents behavior of the device under a failure;
• determining uncertainty associated with the synthetic data;
• determining a set of prediction parameters from the uncertainty using an uncertainty quantification model;
• generating training data from the synthetic data based on the set of prediction parameters;
• and
• learning a set of learned parameters associated with the device by using a machine-learning-based classifier or a regression model on the training data.