Optimization of black-box models with uncertain climatic inputs. Application to sunflower ideotype design
Résumé
Accounting for the interannual climatic variations is a well-known issue for simulation-based
studies of environmental systems. It often requires intensive sampling (e.g., averaging the
simulation outputs over many climatic series), which hinders many sequential processes, in
particular optimization algorithms. We propose here an approach based on a subset selection
in a large basis of climatic series, using an ad-hoc similarity function and clustering. A
non-parametric reconstruction technique is introduced to estimate accurately the distribution
of the output of interest using only the subset sampling. The proposed strategy is non-intrusive
and generic (i.e. transposable to most models with climatic data inputs), and can be
combined to most ªoff-the-shelfº optimization solvers. We apply our approach to sunflower
ideotype design using the crop model SUNFLO. The underlying optimization problem is formulated
as a multi-objective one to account for risk-aversion. Our approach achieves good
performances even for limited computational budgets, outperforming significantly standard
strategies.
Domaines
Sciences du Vivant [q-bio]
Origine : Fichiers éditeurs autorisés sur une archive ouverte
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