125 citations · 208 across the 22 of their papers we have counts for
12 papers · 1 filter
Energy-aware frugal Bayesian optimization
Gaston Plat, Paul Saves, Nathalie Bartoli +2
Modern design optimization frameworks aim first and foremost for models with the most accurate predictions without balancing computational overhead. It remains a reason why scaled…
From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models
Paul Saves, Matthieu Mastio, Nicolas Verstaevel +1
Systematic exploration of Agent-Based Models (ABMs) is challenged by the curse of dimensionality and their inherent stochasticity. We present a multi-stage pipeline integrating the…
Analyzing Shapley Additive Explanations to Understand Anomaly Detection Algorithm Behaviors and Their Complementarity
Jordan Levy, Paul Saves, Moncef Garouani +2
Unsupervised anomaly detection is a challenging problem due to the diversity of data distributions and the lack of labels. Ensemble methods are often adopted to mitigate these chal…
Global Sensitivity Analysis for Engineering Design Based on Individual Conditional Expectations
Pramudita Satria Palar, Paul Saves, Rommel G. Regis +4
Explainable machine learning techniques have gained increasing attention in engineering applications, especially in aerospace design and analysis, where understanding how input var…
Frequency-aware Surrogate Modeling With SMT Kernels For Advanced Data Forecasting
Nicolas Gonel, Paul Saves, Joseph Morlier
This paper introduces a comprehensive open-source framework for developing correlation kernels, with a particular focus on user-defined and composition of kernels for surrogate mod…
Modeling Hierarchical Spaces: A Review and Unified Framework for Surrogate-Based Architecture Design
Paul Saves, Edward Hallé-Hannan, Jasper Bussemaker +2
Simulation-based problems involving mixed-variable inputs frequently feature domains that are hierarchical, conditional, heterogeneous, or tree-structured. These characteristics po…
Multi-objective Bayesian Optimization With Mixed-categorical Design Variables for Expensive-to-evaluate Aeronautical Applications
Nathalie Bartoli, Thierry Lefebvre, Rémi Lafage +8
This work aims at developing new methodologies to optimize computational costly complex systems (e.g., aeronautical engineering systems). The proposed surrogate-based method (often…
Surrogate-based optimization of system architectures subject to hidden constraints
Jasper Bussemaker, Paul Saves, Nathalie Bartoli +2
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…
Regularized infill criteria for multi-objective Bayesian optimization with application to aircraft design
Robin Grapin, Youssef Diouane, Joseph Morlier +4
Bayesian optimization is an advanced tool to perform ecient global optimization It consists on enriching iteratively surrogate Kriging models of the objective and the constraints b…
SMT-EX: An Explainable Surrogate Modeling Toolbox for Mixed-Variables Design Exploration
Mohammad Daffa Robani, Paul Saves, Pramudita Satria Palar +2
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 pra…
Bayesian Optimization of a Lightweight and Accurate Neural Network for Aerodynamic Performance Prediction
James M. Shihua, Paul Saves, Rhea P. Liem +1
Ensuring high accuracy and efficiency of predictive models is paramount in the aerospace industry, particularly in the context of multidisciplinary design and optimization processe…
SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes
Paul Saves, Remi Lafage, Nathalie Bartoli +6
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. Thi…