Szczegóły publikacji
Opis bibliograficzny
Adaptive surrogate-assisted optimal sailboat path search using onboard computers / Roman DĘBSKI, Rafał DREŻEWSKI // W: Computational Science – ICCS 2022 : 22nd international conference : London, UK, June 21–23, 2022 : proceedings, Pt. 3 / eds. Derek Groen, Clélia de Mulatier, Maciej Paszyński, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, Peter M. A. Sloot. — Cham : Springer Nature Switzerland, cop. 2022. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 13352). — ISBN: 978-3-031-08756-1; e-ISBN: 978-3-031-08757-8. — S. 355–368. — Bibliogr., Abstr. — Publikacja dostępna online od: 2022-06-15
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 140679 |
|---|---|
| Data dodania do BaDAP | 2022-07-04 |
| DOI | 10.1007/978-3-031-08757-8_30 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2022 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
A new surrogate-assisted dynamic programming based optimal path search algorithm – studied in the context of high-performance sailing – is shown to be both effective and (energy) efficient. The key elements in achieving this – the fast and accurate physics-based surrogate model, the integrated refinement of the solution space and simulation model fidelity, and the OpenCL-based SPMD-parallelisation of the algorithm – are presented in detail. The included numerical results show the high accuracy of the surrogate model (relative approximation error medians smaller than 0.85%), its efficacy in terms of computing time reduction (from 39.2 to 45.4 times), and the high speedup of the parallel algorithm (from 5.5 to 54.2). Combining these effects gives (up to) 2461 times faster execution. The proposed approach can also be applied to other domains. It can be considered as a dynamic programming based optimal path planning framework parameterised by a problem specific (potentially variable-fidelity) cost-function evaluator (surrogate).
