most citedSystem Architecture Optimization Strategies: Dealing with Expensive Hierarchical Problems

11 citations · 47 across the 8 of their papers we have counts for

collaborators

11 papers

cs.AI20251 cited

Surrogate Modeling and Explainable Artificial Intelligence for Complex Systems: A Workflow for Automated Simulation Exploration

Paul Saves, Pramudita Satria Palar, Muhammad Daffa Robani +6

Complex systems are increasingly explored through simulation-driven engineering workflows that combine physics-based and empirical models with optimization and analytics. Despite t…

cs.LG2025

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…

cs.LG2025

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…

cs.LG20253 cited

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…

cs.LG20257 cited

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…

stat.ME20259 cited

Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design

Paul Saves, Nathalie Bartoli, Youssef Diouane +5

Multidisciplinary design optimization methods aim at adapting numerical optimization techniques to the design of engineering systems involving multiple disciplines. In this context…