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In this paper, we propose a novel sequential data-driven method for dealing with equilibrium based chemical simulations, which can be seen as a specific machine learning approach called active learning. The underlying idea of our approach is to consi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b8c8e854a47bf3884aa3db8a1008bb0d
http://arxiv.org/abs/2110.08111
http://arxiv.org/abs/2110.08111
