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The networks took only the few biogeochemical model parameters and attempted to predict the spatially distributed concentrations of the ecosystem, in this case only nutrients, for one time point of the annual cycle. The ocean circulation was fixed for all parameters. Different network topologies, sparse networks, and hyperparameter optimization using a\u00a0genetic algorithm were used. This showed that all studied networks can produce a\u00a0distribution that is point-wise close to the original spin-up result. However, these predictions were far from being annually periodic, such that a\u00a0subsequent spin-up was necessary. In this way, the overall runtime of the spin-up could be reduced by 13% on average. It is debatable whether this procedure is useful for the generation of initial values, or whether simpler methods can achieve faster convergence.<\/jats:p>","DOI":"10.1007\/s00287-022-01491-y","type":"journal-article","created":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T12:02:27Z","timestamp":1665230547000},"page":"304-308","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Can neural networks predict steady annual cycles of marine ecosystems?","K\u00f6nnen neuronale Netzwerke stetige Jahreszyklen mariner \u00d6kosysteme vorhersagen?"],"prefix":"10.1007","volume":"45","author":[{"given":"Thomas","family":"Slawig","sequence":"first","affiliation":[]},{"given":"Markus","family":"Pfeil","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,8]]},"reference":[{"issue":"3-4","key":"1491_CR1","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.pocean.2010.05.002","volume":"86","author":"I Kriest","year":"2010","unstructured":"Kriest\u00a0I, Khatiwala\u00a0S, Oschlies\u00a0A (2010) Towards an assessment of simple global marine biogeochemical models of different complexity. 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