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• W2896857657 abstract "In numerous applications, forecasting relies on numerical solvers for partial differential equations (PDEs). Although the use of deep-learning techniques has been proposed, the uses have been restricted by the fact the training data are obtained using PDE solvers. Thereby, the uses were limited to domains, where the PDE solver was applicable, but no further. We present methods for training on small domains, while applying the trained models on larger domains, with consistency constraints ensuring the solutions are physically meaningful even at the boundary of the small domains. We demonstrate the results on an air-pollution forecasting model for Dublin, Ireland." @default.
• W2896857657 created "2018-10-26" @default.
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• W2896857657 date "2018-09-27" @default.
• W2896857657 modified "2023-09-27" @default.
• W2896857657 title "Scaling up Deep Learning for PDE-based Models" @default.
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• W2896857657 hasPublicationYear "2018" @default.
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