System and method for model parameter optimization

A computer system comprising:

• a processor;
• and
• a storage device coupled to the processor and storing instructions which when executed by the processor cause the processor to perform a method, the method comprising:
• obtaining, using a first machine-learning technique, a parameter-transform model for mapping parameters of a numerical model other than the parameter-transform model at a first resolution to parameters of the numerical model at a second resolution, wherein the second resolution is higher than the first resolution, and obtaining the parameter-transform model comprises:
• obtaining parameter-transform training samples comprising a set of training parameters of the numerical model associated with the first resolution and a corresponding set of training parameters of the numerical model associated with the second resolution;
• and
• training the parameter-transform model using the obtained parameter-transform training samples;
• performing a parameter-tuning operation on the numerical model at the first resolution to obtain a first set of tuned parameters;
• and
• applying the parameter-transform model on the first set of tuned parameters to obtain a second set of tuned parameters at the second resolution;
• generating behavior information associated with the physical system by running the numerical model at the second resolution using the second set of tuned parameters;
• and
• obtaining, using a second machine-learning technique, a parameter-mapping model for mapping an output of the numerical model to actual parameters used for running the numerical model at the first resolution.

A computer-implemented method comprising:

• obtaining, using a machine-learning technique, a parameter-transform model for mapping parameters of a numerical model other than the parameter-transform model at a first resolution to parameters of the numerical model at a second resolution, wherein the second resolution is higher than the first resolution, the numerical model comprises a deep regression network, and the parameter-transform model comprises a gradient-boosting regression model;
• performing a parameter-tuning operation on the numerical model at the first resolution to obtain a first set of tuned parameters;
• applying the parameter-transform model on the first set of tuned parameters to obtain a second set of tuned parameters at the second resolution;
• and
• generating behavior information associated with the physical system by running the numerical model at the second resolution using the second set of tuned parameters.