12 papers
Efficient multidisciplinary design via Bayesian optimization
Nathalie Bartoli, Thierry Lefebvre, Rémi Lafage +3
This study introduces SEGOMOE, a Bayesian optimization tool for optimizing complex, computationally expensive systems, especially in aeronautics. It efficiently handles mixed desig…
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…
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…
Geometry aware inference of steady state PDEs using Equivariant Neural Fields representations
Giovanni Catalani, Michael Bauerheim, Frédéric Tost +2
Advances in neural operators have introduced discretization invariant surrogate models for PDEs on general geometries, yet many approaches struggle to encode local geometric struct…
Efficient Multi-Objective Constrained Bayesian Optimization of Bridge Girder
Heine Havneraas Røstum, Joseph Morlier, Sebastien Gros +1
The buildings and construction sector is a significant source of greenhouse gas emissions, with cement production alone contributing 7~\% of global emissions and the industry as a…
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…