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Binghang Lu, Christian Moya and Guang Lin
This paper presents NSGA-PINN, a multi-objective optimization framework for the effective training of physics-informed neural networks (PINNs). The proposed framework uses the non-dominated sorting genetic algorithm (NSGA-II) to enable traditional stocha...
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Chady Ghnatios, Victor Champaney, Angelo Pasquale and Francisco Chinesta
In many contexts of scientific computing and engineering science, phenomena are monitored over time and data are collected as time-series. Plenty of algorithms have been proposed in the field of time-series data mining, many of them based on deep learnin...
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Elisa Mammoliti, Davide Fronzi, Adriano Mancini, Daniela Valigi and Alberto Tazioli
Nowadays, the balance between incoming precipitation and stream or spring discharge is a challenging aspect in many scientific disciplines related to water management. In this regard, although advances in the methodologies for water balance calculation c...
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Julian M. Kunkel,Luciana R. Pedro
Pág. 35 - 53
The efficient, convenient, and robust execution of data-driven workflows and enhanced data management are essential for productivity in scientific computing. In HPC, the concerns of storage and computing are traditionally separated and optimise...
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