
Paul SavesOffice National d'Études et de Recherches Aérospatiales | ONERA · DCPS - Department of System Design and Performance Evaluation
Paul Saves
Ph.D & M. Eng. & M. Sc.
Aerospace Engineering. AIAA Best Paper 2022 & 2024. Best PhD ISAE-SUPAERO 2024.
About
31
Publications
4,963
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184
Citations
Introduction
MSc & MEng & PhD. Numerical Models, Operations Research.
2023 AIAA MDO BEST PAPER https://arc.aiaa.org/doi/abs/10.2514/6.2022-0082
Additional affiliations
February 2025 - present
March 2020 - February 2025
Position
- Toulouse
Description
- Ecological aircraft Design optimization. This work is part of the "eXtended-Overall Aircraft Design" federation between ENAC, ISAE and ONERA and targets ecological aeronautics improvements for the European projects "AGILE 4.0" and “COLOSSUS” through Systems of Systems modeling and optimization. Multidisciplinary Design Optimization for Overall Aircraft Design at the conceptual level.
June 2016 - June 2016
Ministère des armées
Position
- Worker Intern
Description
- Landing systems testing
Education
October 2020 - October 2023
September 2019 - September 2020
January 2019 - May 2019
Publications
Publications (31)
Choosing the right system architecture for the problem at hand is challenging due to the large design space and high uncertainty in the early stage of the design process. Formulating the architecting process as an optimization problem may mitigate some of these challenges. This work investigates strategies for solving System Architecture Optimizati...
Surrogate models are of high interest for many engineering applications, serving as cheap-to-evaluate time-efficient approximations of black-box functions to help engineers and practitioners make decisions and understand complex systems. As such, the need for explainability methods is rising and many studies have been performed to facilitate knowle...
Bayesian optimization (BO) is one of the most powerful strategies to solve computationally expensive-to-evaluate blackbox optimization problems. However, BO methods are conventionally used for optimization problems of small dimension because of the curse of dimensionality. In this paper, a high-dimensionnal optimization method incorporating linear...
Choosing the right system architecture for the problem at hand is challenging due to the large design space and high uncertainty in the early stage of the design process. Formulating the architecting process as an optimization problem may mitigate some of these challenges. This work investigates strategies for solving system architecture optimizati...
Bayesian optimization (BO) is one of the most powerful strategies to solve computationally expensive-to-evaluate blackbox optimization problems. However, BO methods are conventionally used for optimization problems of small dimension because of the curse of dimensionality. In this paper, to solve high dimensional optimization problems, we propose t...
System Architecture Optimization (SAO) can support the design of novel architectures by formulating the architecting process as an optimization problem. The exploration of novel architectures requires physics-based simulation due to a lack of prior experience to start from, which introduces two specific challenges for optimization algorithms: evalu...
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ODAS 2024: 24th joint ONERA-DLR Aerospace Symposium
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For developing innovative systems architectures, modeling and optimization techniques have been central to frame the architecting process and define the optimization and modeling problems. In this context, for system-of-systems the use of efficient dedicated approaches (often physics-based...
Heterogeneous datasets emerge in various machine learning and optimization applications that feature different input sources, types or formats. Most models or methods do not natively tackle heterogeneity. Hence, such datasets are often partitioned into smaller and simpler ones, which may limit the generalizability or performance, especially if data...
Recently, there has been a growing interest in mixed-categorical metamodels based on Gaussian Process (GP) for Bayesian optimization. In this context, different approaches can be used to build the mixed-categorical GP. Many of these approaches involve a high number of hyperparameters; in fact, the more general and precise the strategy used to build...
Nowadays, there is a significant and growing interest in improving the efficiency of vehicle design processes through the development of tools and techniques in the field of MDO. Specifically, in aerostructure design, aerodynamic and structural variables influence each other and have a joint effect on quantities of interest like weight or fuel cons...
The 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 mixe...
Nowadays, there is a significant and growing interest in improving the efficiency of vehicle design processes through the development of tools and techniques in the field of MDO. Specifically, in aerostructure design, aerodynamic and structural variables influence each other and have a joint effect on quantities of interest like weight or fuel cons...
Multidisciplinary design optimization (MDO) methods aim at adapting numerical optimization techniques to the design of engineering systems involving multiple disciplines. In this context, a large number of mixed continuous, integer, and categorical variables might arise during the optimization process, and practical applications involve a significa...
This 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...
Bayesian optimization (BO) is one of the most powerful strategies to solve expensive black-box optimization problems. However, BO methods are conventionally used for optimization problems of small dimension because of the curse of dimensionality. In this paper, to solve high dimensional optimization problems, we propose to incorporate linear embedd...
The 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 mixe...
Multidisciplinary design optimization methods aim at adapting numerical optimization techniques to the design of engineering systems involving multiple disciplines. In this context, a large number of mixed continuous, integer and categorical variables might arise during the optimization process and practical applications involve a large number of d...
Recently, there has been a growing interest for mixed-categorical meta-models based on Gaussian process (GP) surrogates. In this setting, several existing approaches use different strategies either by using continuous kernels (e.g., continuous relaxation and Gower distance based GP) or by using a direct estimation of the correlation matrix. In this...
Recently, there has been a growing interest for mixed-categorical meta-models based on Gaussian process (GP) surrogates. In this setting, several existing approaches use different strategies either by using continuous kernels (e.g., continuous relaxation and Gower distance based GP) or by using a direct estimation of the correlation matrix. In this...
Bayesian optimization is an advanced tool to perform efficient global optimization. It consists on enriching iteratively surrogate Kriging models of the objective and the constraints (both supposed to be computationally expensive) of the targeted optimization problem. Nowadays, efficient extensions of Bayesian optimization to solve expensive multi-...
Recently, there has been a growing interest for mixed categorical meta-models based on Gaussian process (GP) surrogates. In this setting, several existing approaches use different strategies. Among the recently developed methods, we could cite: GP models built using continuous relaxation of the variables, Gower distance based models or GP models de...
Recently, there has been a growing interest for mixed categorical meta-models based on Gaussian process (GP) surrogates. In this setting, several existing approaches use different strategies. Among the recently developed methods, we could cite: continuous relaxation of the variables, Gower distance based model or GP model based on direct estimation...
Multidisciplinary design optimization methods aim at adapting numerical optimization techniques to the design of engineering systems involving multiple disciplines. In this context, a large number of mixed continuous, integer and categorical variables might arise during the optimization process and practical applications involve a large number of d...
The paper has been renamed: Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design. More: https://arc.aiaa.org/doi/10.2514/6.2022-0082
Multidisciplinary Design Optimization (MDO) methods aim at adapting numerical optimization techniques to the design of engineering systems involving multiple disciplines or components. Among MDO architectures, various ones are considering the resolution of the Multidisciplinary Design Analysis (MDA). In our study, the system of interest being an ai...
High-dimensional constrained Bayesian optimization with mixed integer variables using continuous relaxation, supper efficient global optimization and kriging with partial least squares for multidisciplinary aircraft optimization.
Recent advances in aircraft design and multidisciplinary formulation have
led to new work
ows involving many disciplines, ranging up to high levels of
�delity codes. However, there is still a lack of information about the di�erent
available optimization methods and their e�ciency depending on the problem
properties.
Aeronautics design is especially...
This report focuses on the development and analysis of algorithms for signal and image processing, with a particular emphasis on orthogonal transformations, steganography, and strip transform techniques. The study includes theoretical research, implementation of algorithms, and evaluation of their performance.