US 12,169,776 B2
Superresolution and consistency constraints to scale up deep learning models
Fearghal O'Donncha, Galway (IE); Ambrish Rawat, Dublin (IE); Sean A. McKenna, Reno, NV (US); and Mathieu Sinn, Dublin (IE)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Dec. 15, 2020, as Appl. No. 17/121,933.
Prior Publication US 2022/0188629 A1, Jun. 16, 2022
Int. Cl. G06N 3/08 (2023.01); G06N 3/04 (2023.01)
CPC G06N 3/08 (2013.01) [G06N 3/04 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
at least one processor; and
at least one memory that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations, comprising:
determining 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,
wherein determining the scaling ratio comprises:
extracting patches, comprising overlapping boundaries, from the low mesh resolution predictive data and representing each extracting patch as a high-dimensional vector,
non-linearly mapping each high-dimensional vector onto another high-dimensional vector from the high-resolution observational or ground-truth data, and
aggregating high-resolution patch-wise representations corresponding to each non-linearly mapped vector to generate the high-resolution observational or ground-truth data; and
generating 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.