Scientific Publications

Journals Conferences Preprints Software Talks
Full Bibliography (PDF)

Research Impact

550+

Citations (approx.)

Google Scholar

9+

Q1 Journal Papers

HAL Profile

41+

Co-authors Network

DBLP Graph

Journal Articles

First-author articles

[J1] P. Saves, E. Hallé-Hannan, J. Bussemaker, Y. Diouane, and N. Bartoli, "Hierarchical Modeling and Architecture Optimization: Review and Unified Framework," Structural and Multidisciplinary Optimization, vol. 69, no. 3, 2026. DOI CS Q1
[J2] P. Saves, R. Lafage, N. Bartoli, Y. Diouane, J. Bussemaker, et al., "SMT 2.0: A Surrogate Modeling Toolbox with Hierarchical and Mixed Variables Gaussian Processes," Advances in Engineering Software, vol. 188, 2024. DOI CS Q1
[J3] P. Saves, Y. Diouane, N. Bartoli, T. Lefebvre, and J. Morlier, "High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraft," Structural and Multidisciplinary Optimization, 2024. DOI CS Q1
[J4] P. Saves, Y. Diouane, N. Bartoli, T. Lefebvre, and J. Morlier, "A mixed-categorical correlation kernel for Gaussian process," Neurocomputing, vol. 558, 2023. DOI CS Q1

Second-author articles

[J5] P. S. Palar, P. Saves, M. D. Robani, N. Verstaevel, M. Garouani, J. Aligon, K. Shimoyama, J. Morlier, B. Gaudou, "Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making," Archives of Computational Methods in Engineering, 2026. DOI CS Q1 and Maths Q1
[J6] P. Palar, P. Saves, R. G. Regis, K. Shimoyama, S. Obayashi, et al., "Global Sensitivity Analysis For Engineering Design Based on Individual Conditional Expectations," Aerospace Science and Technology, 2026. DOI Engineering Q1
[J7] J. H. Bussemaker, P. Saves, N. Bartoli, T. Lefebvre, and R. Lafage, "System Architecture Optimization Strategies: Dealing with Expensive Hierarchical Problems," Journal of Global Optimization, 2024. DOI Maths Q1

Last-author articles

[J8] E. Hallé-Hannan, C. Audet, Y. Diouane, S. Le Digabel, and P. Saves, "A distance for mixed-variable and hierarchical domains with meta variables," Neurocomputing, 2025. DOI CS Q1
[J9] R. Priem, Y. Diouane, N. Bartoli, S. Dubreuil, and P. Saves, "High-dimensional Bayesian optimization using both random and supervised embeddings," AIAA Journal, 2024. DOI Engineering Q1

Conference Proceedings

First-author articles

[C1] P. Saves, M. Mastio, N. Verstaevel, and B. Gaudou, "From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models," in MABS 2026 - The 27th International Workshop on Multi-Agent-Based Simulation, Springer, 2026. HAL CORE C
[C2] P. Saves, N. Verstaevel, and B. Gaudou, "Modèles de substitution pour les modèles basés agents : enjeux, méthodes et applications," in Proc. JFSMA 2025, Dijon, France, 2025. HAL CORE National
[C3] P. Saves, J. Bussemaker, R. Lafage, T. Lefebvre, and N. Bartoli, "System-of-systems modeling and optimization: an integrated framework for intermodal mobility," in Proc. ODAS 2024 (24th ONERA-DLR Symposium), Braunschweig, Germany, 2024. HAL
[C4] P. Saves, N. Bartoli, Y. Diouane, T. Lefèbvre, and J. Morlier, "Multidisciplinary Design Optimization with Mixed Categorical Variables for Aircraft Design," in Proc. AIAA SciTech 2023 Forum, National Harbor, USA, 2023. DOI
[C5] P. Saves, E. Nguyen Van, N. Bartoli, T. Lefebvre, and C. David, "Bayesian Optimization for Mixed Variables Using an Adaptive Dimension Reduction Process: Applications to Aircraft Design," in Proc. AIAA SciTech 2022 Forum, San Diego, USA, 2022. - AIAA Best Paper Award (all conferences) - DOI
[C6] P. Saves, Y. Diouane, N. Bartoli, T. Lefebvre, and J. Morlier, "A General Square Exponential Kernel to Handle Mixed-Categorical Variables for Gaussian Process," in Proc. AIAA AVIATION 2022 Forum, Chicago, USA, 2022. DOI
[C7] P. Saves, N. Bartoli, Y. Diouane, T. Lefebvre, and J. Morlier, "Constrained Bayesian optimization over mixed categorical variables, with application to aircraft design," in Proc. ECCOMAS AeroBest 2021, Lisboa, Portugal, 2021. HAL

Second-author articles

[C8] M. Mastio, P. Saves, B. Gaudou, and N. Verstaevel, "Adaptive Agents in Spatial Double-Auction Markets: Modeling the Emergence of Industrial Symbiosis," in Proc. AAMAS 2026, Paphos, Cyprus, 2026. DOI CORE A*
[C9] J. Levy, P. Saves, M. Garouani, N. Verstaevel, and B. Gaudou, "Analyzing Shapley additive explanations to understand anomaly detection algorithm behaviors and their complementarity," in Advances in Intelligent Data Analysis XXIV, Leiden, Netherlands, Springer, 2026 - Springer IDA Frontier Prize - DOI CORE B
[C10] M. D. Robani, P. Saves, L. R. Zuhal, P. S. Palar, and J. Morlier, "SMT-EX: An Explainable Surrogate Modeling Toolbox for Mixed-Variables Design Exploration," in Proc. AIAA SciTech 2025 Forum, Orlando, USA, 2025. DOI
[C11] N. Gonel, P. Saves, and J. Morlier, "Frequency-aware Surrogate Modeling With SMT Kernels For Advanced Data Forecasting," in Proc. ECCOMAS AeroBest 2025, Lisboa, Portugal, 2025. HAL
[C12] J. H. Bussemaker, P. Saves, N. Bartoli, T. Lefebvre, and B. Nagel, "Surrogate-Based Optimization of System Architectures Subject to Hidden Constraints," in Proc. AIAA Aviation 2024 Forum, Las Vegas, USA, 2024. - Best Student Paper Award - DOI

Last-author articles

[C13] R. Priem, N. Bartoli, Y. Diouane, S. Dubreuil, and P. Saves, "High-Dimensional Efficient Global Optimization Using Both Random and Supervised Embeddings," in Proc. AIAA AVIATION 2023 Forum, San Diego, USA, 2023. DOI

Other co-authored articles

[C14] N. Bartoli, T. Lefebvre, R. Lafage, P. Saves, Y. Diouane, J. Morlier, L. Pretsch, "Multi-objective Bayesian optimization with mixed-categorical design variables for expensive-to-evaluate aeronautical applications," in Proc. MOPTA 2025, Ponta Delgada, Portugal, 2025. HAL CORE National
[C15] N. Bartoli, T. Lefebvre, R. Lafage, P. Saves, Y. Diouane, J. Morlier, J. Bussemaker, G. Donelli, J. M. Gomes de Mello, M. Mandorino, P. Della Vechia, "Multi-objective Bayesian optimization with mixed-categorical design variables for expensive-to-evaluate aeronautical applications," in Proc. ECCOMAS AeroBest 2023, Lisboa, Portugal, 2023. HAL
[C16] R. Grapin, Y. Diouane, J. Morlier, N. Bartoli, and T. Lefebvre, P. Saves, J. H. Bussemaker, "Regularized Infill Criteria for Multi-Objective Bayesian Optimization with Application to Aircraft Design," in Proc. AIAA AVIATION 2022, Chicago, USA, 2022. DOI

Preprints & Working Papers

[P1] P. Saves, P. S. Palar, M. D. Robani, N. Verstaevel, M. Garouani, J. Aligon, K. Shimoyama, J. Morlier, B. Gaudou, "Surrogate Modeling and Explainable Artificial Intelligence for Complex Systems: A Workflow for Automated Simulation Exploration," arXiv:2510.16742, 2025. ArXiv
[P2] J. Shihua, P. Saves, R. Liem, and J. Morlier, "Bayesian Optimization of a Lightweight and Accurate Neural Network for Aerodynamic Performance Prediction," 2026. ArXiv

Theses

[T1] P. Saves, "High-dimensional multidisciplinary design optimization for aircraft eco-design," Ph.D. dissertation, ISAE-SUPAERO / ONERA, 2024. HAL (Thesis)

Scientific Software

- French Open Science Award 2026 -

SMT 2.0: Surrogate Modeling Toolbox

Open Source BSD-3

Lead Developer & Maintainer

SMT is a Python toolbox for surrogate modeling, Bayesian optimization, and design-space exploration, developed for researchers and engineers. The framework has been applied to diverse high-stakes engineering problems including rocket engine injectors, aircraft fuel consumption modeling, high-order finite element methods, solar energy planning, and wind turbine design.

Main Core Design-space-ext Explainability

design-space-ext supports mixed-discrete and hierarchical design variables, while explainability provides automated XAI metrics, sensitivity analysis, and visualization workflows.

SEGOMOE: Mixture-of-Experts Bayesian Optimizer

Proprietary

Lead Developer & Maintainer · Registered software co-inventor (French software deposit #2948)

High-performance constrained Bayesian optimization framework co-developed by ISAE-SUPAERO and ONERA. I restructured the core architecture and integrated advanced capabilities including multi-fidelity, multi-objective optimization, heterogeneous variable handling, and scalable surrogate-assisted exploration.

Technical Reference

GAMA Platform: Agent-Based Modeling & Simulation

Open Source GPL-3

Regular Developer

GAMA is an advanced open-source platform for spatially explicit multi-agent simulation. My work primarily focuses on the development of exploration and optimization tools, as well as contributing as a regular developer to a specific GAML model for agro-ecology.

Website GitHub Documentation

The platform enables large-scale, GIS-integrated socio-ecological modeling using the high-level GAML language.

All research algorithms are implemented across these frameworks. While datasets are not public by default, they are available upon request, and most numerical experiments can be reproduced for validation.

Oral Communications & Selected Talks

  • 2025: Assises nationales des données de la recherche (ANDOR 2025), Saclay ENS, France. (Remise de prix Science Ouverte de la thèse).
  • 2025: Towards Sustainable Aviation Summit, Toulouse, France. "Sustainable Eco-design of Hybrid-electric Aircraft."
  • 2025: Docteur, raconte-moi ta thèse en 3 minutes !, Toulouse, France. (Prix de thèse de la Fondation ISAE-SUPAERO).
  • 2025: ANR MIMICO 1st Workshop, Toulouse, France. "Explainability with mixed variables applied to MAS."
  • 2024: Workshop on Bayesian optimization & related topics, Paris, France.
  • 2021: Surrogate Day, Organizer & Speaker, ISAE-SUPAERO.
  • 2021: Enhanced kriging models within a Bayesian optimization framework, to handle both continuous and categorical inputs, Forth Worth, USA, SIAM Conference on Computational Science and Engineering.
  • 2024: A graph-structured distance for heterogeneous datasets with meta variables , Toulouse, France, AISSAI (AI for Science).