Markus Pfeil,
Thomas Slawig
S. 138 - 156
doi:
10.1553/etna_vol56s138
Verlag der Österreichischen Akademie der Wissenschaften
doi:
10.1553/etna_vol56s138
Abstract:
Marine ecosystem models are important to identify the processes that affect for example the global carbon cycle. Computation of an annually periodic solution (i.e., a steady annual cycle) for these models requires a high computational effort. To reduce this effort, we approximate an exemplary marine ecosystem model by different artificial neural networks (ANNs). We use a fully connected network (FCN), then apply the sparse evolutionary training (SET) procedure, and finally apply a genetic algorithm (GA) to optimize, inter alia, the network topology. With all three approaches, a direct approximation of the steady annual cycle is not sufficiently accurate. However, using the mass-corrected prediction of the ANN as initial concentration for additional model runs, the results are in very good agreement. In this way, we achieve a runtime reduction by about 15%. The results from the SET algorithm are comparable to those of the FCN. Further application of the GA may lead to an even higher reduction.
deep learning, genetic algorithm, sparse evolutionary training, biogeochemical modeling, marine ecosystem modeling, transport matrix method
Published Online:
2022/01/10 15:30:43
Object Identifier:
0xc1aa5576 0x003d2e1a
Rights:All rights reserved.For questions regarding copyright and copies please contact us by email.
Electronic Transactions on Numerical Analysis (ETNA) is an electronic journal for the publication of significant new developments in numerical analysis and scientific computing. Papers of the highest quality that deal with the analysis of algorithms for the solution of continuous models and numerical linear algebra are appropriate for ETNA, as are papers of similar quality that discuss implementation and performance of such algorithms. New algorithms for current or new computer architectures are appropriate provided that they are numerically sound. However, the focus of the publication should be on the algorithm rather than on the architecture. The journal is published by the Kent State University Library in conjunction with the Institute of Computational Mathematics at Kent State University, and in cooperation with the Johann Radon Institute for Computational and Applied Mathematics of the Austrian Academy of Sciences (RICAM). Reviews of all ETNA papers appear in Mathematical Reviews and Zentralblatt für Mathematik. Reference information for ETNA papers also appears in the expanded Science Citation Index. ETNA is registered with the Library of Congress and has ISSN 1068-9613.
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