Superresolution and consistency constraints to scale up deep learning models
US12169776B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 15, 2020 |
| Grant date | Dec 17, 2024 |
| Priority date | — |
| Expiry date | Oct 18, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/045
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques of facilitating deep learning model rescaling by computing devices. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise: a rescaling component; and a forecasting component. The rescaling component can determine 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. The forecasting component can generate 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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.