Zobrazeno 1 - 4
of 4
pro vyhledávání: '"David Gaudrie"'
Publikováno v:
Structural and Multidisciplinary Optimization
Structural and Multidisciplinary Optimization, 2020, 61, pp.2343-2361. ⟨10.1007/s00158-019-02458-6⟩
Structural and Multidisciplinary Optimization, Springer Verlag (Germany), 2020, 61, pp.2343-2361. ⟨10.1007/s00158-019-02458-6⟩
Structural and Multidisciplinary Optimization, Springer Verlag (Germany), 2019
Structural and Multidisciplinary Optimization, 2020, 61, pp.2343-2361. ⟨10.1007/s00158-019-02458-6⟩
Structural and Multidisciplinary Optimization, Springer Verlag (Germany), 2020, 61, pp.2343-2361. ⟨10.1007/s00158-019-02458-6⟩
Structural and Multidisciplinary Optimization, Springer Verlag (Germany), 2019
International audience; Parametric shape optimization aims at minimizing an objective function f(x) where x are CAD parameters. This task is difficult when f is the output of an expensive-to-evaluate numerical simulator and the number of CAD paramete
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39c163582fc96512046dbe45460f0420
https://hal-emse.ccsd.cnrs.fr/emse-02390627
https://hal-emse.ccsd.cnrs.fr/emse-02390627
Publikováno v:
Annals of Mathematics and Artificial Intelligence
Annals of Mathematics and Artificial Intelligence, 2019, pp 1-26. ⟨10.1007/s10472-019-09644-8⟩
Annals of Mathematics and Artificial Intelligence, Springer Verlag, 2019, pp 1-26. ⟨10.1007/s10472-019-09644-8⟩
Annals of Mathematics and Artificial Intelligence, 2019, pp 1-26. ⟨10.1007/s10472-019-09644-8⟩
Annals of Mathematics and Artificial Intelligence, Springer Verlag, 2019, pp 1-26. ⟨10.1007/s10472-019-09644-8⟩
International audience; Multi-objective optimization aims at finding trade-off solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensive-to-evaluate functions, it is impossible and often non-informative
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b8dc62abed72f59a83c95fd4b495ba66
https://hal-emse.ccsd.cnrs.fr/emse-01957614
https://hal-emse.ccsd.cnrs.fr/emse-01957614
Targeting Well-Balanced Solutions in Multi-Objective Bayesian Optimization under a Restricted Budget
Publikováno v:
Lecture Notes in Computer Science
12th International Conference on Learning and Intelligent Optimization
12th International Conference on Learning and Intelligent Optimization, Jun 2018, Kalamata, Greece. pp 175-179, ⟨10.1007/978-3-030-05348-2_15⟩
PGMO Days 2017
PGMO Days 2017, Nov 2017, Saclay, France
Journées du GdR Mascot-Num 2018
Journées du GdR Mascot-Num 2018, Mar 2018, Nantes, France
Journées Oquaido 2017
Journées Oquaido 2017, Nov 2017, Orléans, France. 2017
International Conference on Learning and Intelligent Optimization (LION'18)
International Conference on Learning and Intelligent Optimization (LION'18), Jun 2018, Kalamata, Greece
Lecture Notes in Computer Science ISBN: 9783030053475
LION
12th International Conference on Learning and Intelligent Optimization
12th International Conference on Learning and Intelligent Optimization, Jun 2018, Kalamata, Greece. pp 175-179, ⟨10.1007/978-3-030-05348-2_15⟩
PGMO Days 2017
PGMO Days 2017, Nov 2017, Saclay, France
Journées du GdR Mascot-Num 2018
Journées du GdR Mascot-Num 2018, Mar 2018, Nantes, France
Journées Oquaido 2017
Journées Oquaido 2017, Nov 2017, Orléans, France. 2017
International Conference on Learning and Intelligent Optimization (LION'18)
International Conference on Learning and Intelligent Optimization (LION'18), Jun 2018, Kalamata, Greece
Lecture Notes in Computer Science ISBN: 9783030053475
LION
International audience; Multi-objective optimization aims at finding the Pareto set composed of all the best trade-off solutions between several objectives. When dealing with expensive-to-evaluate black box functions, surrogate-based approaches, in t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bab8d02ec56c6440a6c43762f6d50067
https://hal.archives-ouvertes.fr/hal-01883336/document
https://hal.archives-ouvertes.fr/hal-01883336/document
In this paper, we propose an algorithm for robustly fusing digital surface models (DSMs) with different ground sampling distances and confidences, using explicit surface priors to obtain locally smooth surface models. Robust fusion of the DSMs is ach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee97858115cc66b3a86624cf41cfa5e1
https://elib.dlr.de/108351/
https://elib.dlr.de/108351/