mlsanityData to 7 Oct 2026

SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes

arXiv 2305.13998 ↗First posted 23 May 2023cs.LG cs.MS math.OCcited by 27

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Abstract (arXiv)

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 mixed-variable surrogate models and hierarchical variables. These types of variables are becoming increasingly important in several surrogate modeling applications. SMT 2.0 also improves SMT by extending sampling methods, adding new surrogate models, and computing variance and kernel derivatives for Kriging. This release also includes new functions to handle noisy and use multifidelity data. To the best of our knowledge, SMT 2.0 is the first open-source surrogate library to propose surrogate models for hierarchical and mixed inputs. This open-source software is distributed under the New BSD license.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 22 of its 97 references that are in the map. First posted on arXiv 23 May 2023.

Builds on

22 of 97 references.

References · 22

  1. 2021SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimizationcited by 229
  2. 2019Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonflycited by 66
  3. 2017Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processescited by 65
  4. 2013Active Learning of Linear Embeddings for Gaussian Processescited by 34
  5. 2018A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factorscited by 30
  6. 2022A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamicscited by 30
  7. 2018Group kernels for Gaussian process metamodels with categorical inputscited by 22
  8. 2019Bayesian Optimization using Deep Gaussian Processescited by 22
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  10. 2017Gradient-enhanced kriging for high-dimensional problemscited by 15
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10 more
  1. 2023Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlowcited by 11
  2. 2020Bayesian optimization of variable-size design space problemscited by 10
  3. 2023OpenBox: A Python Toolkit for Generalized Black-box Optimizationcited by 9
  4. 2020Continuous surrogate-based optimization algorithms are well-suited for expensive discrete problemscited by 8
  5. 2022A general mathematical framework for constrained mixed-variable blackbox optimization problems with meta and categorical variablescited by 8
  6. 2021A comparison of mixed-variables Bayesian optimization approachescited by 6
  7. 2020Multioutput Gaussian Processes with Functional Data: A Study on Coastal Flood Hazard Assessmentcited by 5
  8. 2013A Kernel for Hierarchical Parameter Spacescited by 4
  9. 2020Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processescited by 4
  10. 2018A First Analysis of Kernels for Kriging-based Optimization in Hierarchical Search Spacescited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 16; 2026: 6.

27 papers.

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  1. 2024Aero-Nef: Neural Fields for Rapid Aircraft Aerodynamics Simulationscited by 24
  2. 2025Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulationscited by 10
  3. 2023High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraftcited by 8
  4. 2025Surrogate Modeling and Explainable Artificial Intelligence for Complex Systems: A Workflow for Automated Simulation Explorationcited by 6
  5. 2025Geometry aware inference of steady state PDEs using Equivariant Neural Fields representationscited by 5
  6. 2024A distance for mixed-variable and hierarchical domains with meta variablescited by 4
  7. 2025SMT-EX: An Explainable Surrogate Modeling Toolbox for Mixed-Variables Design Explorationcited by 4
  8. 2025ONERA's CRM WBPN database for machine learning activities, related regression challenge and first resultscited by 4
  9. 2025Surrogate-based optimization of system architectures subject to hidden constraintscited by 3
  10. 2025Multi-objective Bayesian Optimization With Mixed-categorical Design Variables for Expensive-to-evaluate Aeronautical Applicationscited by 3
  11. 2025Modeling Hierarchical Spaces: A Review and Unified Framework for Surrogate-Based Architecture Designcited by 3
  12. 2025SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence eracited by 2
15 more
  1. 2025Bayesian Optimization of a Lightweight and Accurate Neural Network for Aerodynamic Performance Predictioncited by 2
  2. 2025System-of-systems Modeling and Optimization: An Integrated Framework for Intermodal Mobilitycited by 1
  3. 2025A Continuous Encoding-Based Representation for Efficient Multi-Fidelity Multi-Objective Neural Architecture Searchcited by 1
  4. 2025Adaptive Agents in Spatial Double-Auction Markets: Modeling the Emergence of Industrial Symbiosiscited by 1
  5. 2024Jacobian-Enhanced Neural Networkscited by 0
  6. 2024Open-Source High-Speed Flight Surrogate Modeling Frameworkcited by 0
  7. 2025Mod`eles de Substitution pour les Mod`eles `a base d'Agents : Enjeux, M'ethodes et Applicationscited by 0
  8. 2025Frequency-aware Surrogate Modeling With SMT Kernels For Advanced Data Forecastingcited by 0
  9. 2025Global Sensitivity Analysis for Engineering Design Based on Individual Conditional Expectationscited by 0
  10. 2026Practical Efficient Global Optimization is No-regretcited by 0
  11. 2026Multi-fidelity approaches for general constrained Bayesian optimization with application to aircraft designcited by 0
  12. 2026Transfer Learning in Bayesian Optimization for Aircraft Designcited by 0
  13. 2026Robust and Explainable Divide-and-Conquer Learning for Intrusion Detectioncited by 0
  14. 2026Efficient multidisciplinary design via Bayesian optimizationcited by 0
  15. 2026Hierarchical Bayesian optimization of an aircraft-based multi-agent system-of-systemscited by 0