A Python surrogate modeling framework with derivatives

The surrogate modeling toolbox (SMT) is an open-source Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions. This package provides a library of surrogate models that is simple to use and facilitates the implementation of additional methods. SMT is different from existing surrogate modeling libraries because of its emphasis on derivatives, including training derivatives used for gradient-enhanced modeling, prediction derivatives, and derivatives with respect to training data. It also includes unique surrogate models: kriging by partial least-squares reduction, which scales well with the number of inputs; and energy- minimizing spline interpolation, which scales well with the number of training points. The efficiency and effectiveness of SMT are demonstrated through a series of examples. SMT is documented using custom tools for embedding automatically tested code and dynamically generated plots to produce high-quality user guides with minimal effort from contributors. SMT is maintained in a public version control repository

Towards the Industrialization of New MDO Methodologies and Tools for Aircraft Design

An overall summary of the Institute of Technology IRT Saint Exupery MDA-MDO project (Multi-Disciplinary Analysis - Multidisciplinary Design Optimization) is presented. The aim of the project is to develop efficient capabilities (methods, tools and a software platform) to enable industrial deployment of MDO methods in industry. At IRT Saint Exupery, industrial and academic partners collaborate in a single place to the development of MDO methodologies; the advantage provided by this mixed organization is to directly benefit from both advanced methods at the cutting edge of research and deep knowledge of industrial needs and constraints. This paper presents the three main goals of the project: the elaboration of innovative MDO methodologies and formulations (also referred to as architectures in the literature 1) adapted to the resolution of industrial aircraft optimization design problems, the development of a MDO platform featuring scalable MDO capabilities for transfer to industry and the achievement of a simulation-based optimization of an aircraft engine pylon with industrial Computational Fluid Dynamics (CFD) and Computational Structural Mechanics (CSM) tools

Disciplinary surrogates for gradient-based optimization of multidisciplinary systems

International audienceMany engineering problems are described by complex multidisciplinary systems, whose behavior is dictated by a non-linear system of equations called multidisciplinary analysis (MDA). When optimizing these systems, the resolution of the MDA at each evaluated design space point often represents a heavy computational burden, particularly when high-fidelity solvers are used. In this work, we address the high computational cost of the MDA by replacing the disciplinary solvers by Gaussian Process (GP) surrogate models. This approach allows to uncouple the disciplinary solvers, similarly to what is done in the Individual Disciplinary Feasible formulation. Moreover, a procedure for the adaptive enrichment of the disciplinary surrogates is proposed to reduce the uncertainty of the surrogates in the explored regions of the design space. The use of GP surrogates further presents the advantage of an analytical gradient computation, which allows for an easy implementation with gradient-based solvers. The performance of the proposed approach has been tested on the analytical benchmark Sellar test case, as well as an aircraft design problem which couples the aerodynamics and structural disciplines. Both test cases show that the proposed approach requires less disciplinary solver calls than classical gradient-based solvers. Finally, the proposed methodology has been integrated in ONERA's WhatsOpt collaborative environment. WhatsOpt generates the OpenMDAO skeleton code, where the implementations of the disciplines can then be plugged into. Thanks to the graphical interface of WhatsOpt, users can easily implement their models and choose the proposed approach to solve the optimization problem

Improvement of efficient global optimization with application to aircraft wing design

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140620/1/6.2016-4001.pd

SMT 2.0 : Une boîte à outils pour modèles de substitution axée sur les processus gaussiens hiérarchiques et à variables mixtes

International audienceThe Surrogate Modeling Toolbox (SMT) is an open-source Python package that offers a collection of surrogate modeling methods, sampling techniques, and a set of sample problems. This paper presents SMT 2.0, a major new release of SMT that introduces significant upgrades and new features to the toolbox. This release adds the capability to handle mixed-variable surrogate models and hierarchical variables. These types of variables are becoming increasingly important in several surrogate modeling applications. SMT 2.0 also improves SMT by extending sampling methods, adding new surrogate models, and computing variance and kernel derivatives for Kriging. This release also includes new functions to handle noisy and use multi-fidelity data. To the best of our knowledge, SMT 2.0 is the first open-source surrogate library to propose surrogate models for hierarchical and mixed inputs. This open-source software is distributed under the New BSD license. https://github.com/SMTorg/SMT.La boîte à outils de modélisation par modèles de substitution (SMT) est un paquetage Python open-source qui offre une collection de méthodes de modélisation des substituts, des techniques d'échantillonnage et un ensemble d'exemples de problèmes. Cet article présente SMT 2.0, une nouvelle version majeure de SMT qui introduit des améliorations significatives et de nouvelles fonctionnalités à la boîte à outils. Cette version ajoute la possibilité de traiter des modèles de substitution à variables mixtes et des variables hiérarchiques. Ces types de variables deviennent de plus en plus importants dans plusieurs applications de modèles de substitution. SMT 2.0 améliore également SMT en étendant les méthodes d'échantillonnage, en ajoutant de nouveaux modèles de substitution et en calculant la variance et les dérivés du noyau pour le Krigeage. Cette version comprend également de nouvelles fonctions pour traiter les données bruyantes et utiliser des données multi-fidélité. À notre connaissance, SMT 2.0 est la première bibliothèque de substitution à code source ouvert à proposer des modèles de substitution pour des entrées hiérarchiques et mixtes. Ce logiciel libre est distribué sous la nouvelle licence BSD. https://github.com/SMTorg/SMT

MULTI-OBJECTIVE BAYESIAN OPTIMIZATION WITH MIXED-CATEGORICAL DESIGN VARIABLES FOR EXPENSIVE-TO-EVALUATE AERONAUTICAL APPLICATIONS

International audienceThis work aims at developing new methodologies to optimize computational costly complex systems (e.g., aeronautical engineering systems). The proposed surrogate-based method (often called Bayesian optimization) uses adaptive sampling to promote a trade-off between exploration and exploitation. Our in-house implementation, called SEGOMOE, handles a high number of design variables (continuous, discrete or categorical) and nonlinearities by combining mixtures of experts for the objective and/or the constraints. Additionally, the method handles multi-objective optimization settings, as it allows the construction of accurate Pareto fronts with a minimal number of function evaluations. Different infill criteria have been implemented to handle multiple objectives with or without constraints.The effectiveness of the proposed method was tested on practical aeronautical applications within the context of the European Project AGILE 4.0 and demonstrated favorable results. A first example concerns a retrofitting problem where a comparison between two optimizers have been made. A second example introduces hierarchical variables to deal with architecture system in order to design an aircraft family. The third example increases drastically the number of categorical variables as it combines aircraft design, supply chain and manufacturing process. In this article, we show, on three different realistic problems, various aspects of our optimization codes thanks to the diversity of the treated aircraft problems

Identification of new classes among acute myelogenous leukaemias with normal karyotype using gene expression profiling

International audienc

Ostéopathies fragilisantes, maladie rénale chronique, malabsorptions, anomalies biologiques du métabolisme phosphocalcique : les bonnes indications pour un remboursement raisonné du dosage de vitamine D [Weakening osteopathies, chronic kidney disease, malabsorption, biological anomalies of calium/phosphorus metabolism: appropriate indications for a reasoned reimbursment of serum vitamin D measurement]

National audienceEditoria