Superresolution and consistency constraints to scale up deep learning models

A computer-implemented method, comprising:

• determining, by a system operatively coupled to a processor, a scaling ratio that maps low mesh resolution predictive data output by a partial differential equation (PDE)-based model for a sub-domain to high-resolution observational or ground-truth data for a domain comprising the sub-domain;
• and
• generating, by the system, high mesh resolution predictive data for the domain with a machine-learning model using input data of the PDE-based model and the scaling ratio.

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• determine, by the processor, a scaling ratio that maps low mesh resolution predictive data output by a partial differential equation (PDE)-based model for a sub-domain to high-resolution observational or ground-truth data for a domain comprising the sub-domain;
• and
• generate, by the processor, high resolution predictive data for the domain with a machine-learning model using input data of the PDE-based model and the scaling ratio.