Across numerous applications, forecasting relies on numerical solvers for partial differential equations (PDEs). Although the use of deep-learning techniques has been proposed, actual applications have been restricted by the fact the training data are obtained using traditional PDE solvers. Thereby, the uses of deep-learning techniques were limited to domains, where the PDE solver was applicable. We demonstrate a deep-learning framework for air-pollution monitoring and forecasting that provides the ability to train across different model domains, as well as a reduction in the run-time by two orders of magnitude. It presents a first-of-a-kind implementation that combines deep-learning and domain-decomposition techniques to allow model deployments extend beyond the domain(s) on which the it has been trained.
@article{arxiv.1810.09425,
title = {Using Deep Learning to Extend the Range of Air-Pollution Monitoring and Forecasting},
author = {Philipp Haehnel and Jakub Marecek and Julien Monteil and Fearghal O'Donncha},
journal= {arXiv preprint arXiv:1810.09425},
year = {2020}
}