Efficient multidisciplinary design via Bayesian optimization

• URL: http://arxiv.org/abs/2607.22560v1
• Date: Thu, 28 May 2026 16:11:28 GMT
• Title: Efficient multidisciplinary design via Bayesian optimization
• Abstract summary: This study introduces SEGOMOE, a Bayesian optimization tool for optimizing complex, computationally expensive systems, especially in aeronautics.<n>It efficiently handles mixed design variables (continuous, discrete, categorical, hierarchical) using adaptive Gaussian process models.<n>The tool supports multi-fidelity data and solves both single- and multi-objective problems, including hidden constraints and high-dimensional decomposition.
• Score: 0.6157382820537719
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study introduces SEGOMOE, a Bayesian optimization tool for optimizing complex, computationally expensive systems, especially in aeronautics. It efficiently handles mixed design variables (continuous, discrete, categorical, hierarchical) using adaptive Gaussian process models. SEGOMOE combines expert models to address nonlinearities in objectives and constraints, leveraging the open-source Surrogate Modeling Toolbox (SMT). The tool supports multi-fidelity data and solves both single- and multi-objective problems, including hidden constraints and high-dimensional decomposition. Validated through benchmarks and real-world aeronautical applications, SEGOMOE proves to be robust and versatile for tackling multidisciplinary challenges.

Related papers

• Multi-fidelity approaches for general constrained Bayesian optimization with application to aircraft design [0.34998703934432673]
  We show how incorporating information from both the objective and the constraints can further reduce computational costs without compromising the optimality of the solution.<n>We show that employing our proposed multi-fidelity approach leads to $86%$ to $200%$ more constraint compliant solutions given a limited budget.
  arXiv  Detail & Related papers  (2026-03-30T20:36:13Z)
• AlignMamba-2: Enhancing Multimodal Fusion and Sentiment Analysis with Modality-Aware Mamba [56.52470564147458]
  We propose textbfAlignMamba-2, an effective and efficient framework for multimodal fusion and sentiment analysis.<n>Our approach introduces a dual alignment strategy that regularizes the model using both Optimal Transport distance and Maximum Mean Discrepancy.<n>More importantly, we design a Modality-Aware Mamba layer, which employs a Mixture-of-Experts architecture with modality-specific and modality-shared experts.
  arXiv  Detail & Related papers  (2026-03-19T03:47:21Z)
• An Integrated Fusion Framework for Ensemble Learning Leveraging Gradient Boosting and Fuzzy Rule-Based Models [59.13182819190547]
  Fuzzy rule-based models excel in interpretability and have seen widespread application across diverse fields.<n>They face challenges such as complex design specifications and scalability issues with large datasets.<n>This paper proposes an Integrated Fusion Framework that merges the strengths of both paradigms to enhance model performance and interpretability.
  arXiv  Detail & Related papers  (2025-11-11T10:28:23Z)
• Multi-objective Bayesian Optimization With Mixed-categorical Design Variables for Expensive-to-evaluate Aeronautical Applications [0.47812237695718757]
  This work aims at developing new methodologies to optimize computational costly complex systems.<n>The proposed surrogate-based method (often called Bayesian optimization) uses adaptive sampling to promote a trade-off between exploration and exploitation.<n>The effectiveness of proposed method was tested on practical aeronautical applications within the context of the European Project AGILE 4.0.
  arXiv  Detail & Related papers  (2025-04-14T06:44:13Z)
• Vehicle Suspension Recommendation System: Multi-Fidelity Neural Network-based Mechanism Design Optimization [4.038368925548051]
  Vehicle suspensions are designed to improve driving performance and ride comfort, but different types are available depending on the environment. Traditional design process is multi-step, gradually reducing the number of design candidates while performing costly analyses to meet target performance. Recently, AI models have been used to reduce the computational cost of FEA.
  arXiv  Detail & Related papers  (2024-10-03T23:54:03Z)
• Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion [53.33473557562837]
  Solving multi-objective optimization problems for large deep neural networks is a challenging task due to the complexity of the loss landscape and the expensive computational cost. We propose a practical and scalable approach to solve this problem via mixture of experts (MoE) based model fusion. By ensembling the weights of specialized single-task models, the MoE module can effectively capture the trade-offs between multiple objectives.
  arXiv  Detail & Related papers  (2024-06-14T07:16:18Z)
• Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission [74.10928850232717]
  This paper develops generative artificial intelligence (AI) agents for model formulation and then applies a mixture of experts (MoE) to design transmission strategies. Specifically, we leverage large language models (LLMs) to build an interactive modeling paradigm. We propose an MoE-proximal policy optimization (PPO) approach to solve the formulated problem.
  arXiv  Detail & Related papers  (2024-04-14T03:44:54Z)
• Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment [46.44464839353993]
  We introduce Rewards-in-Context (RiC), which conditions the response of a foundation model on multiple rewards in its prompt context. RiC only requires supervised fine-tuning of a single foundation model and supports dynamic adjustment for user preferences during inference time.
  arXiv  Detail & Related papers  (2024-02-15T18:58:31Z)
• Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables [0.3626013617212667]
  A new Quality-Diversity methodology based on mixed variables is proposed in the context of limited simulation budget. The proposed approach provides valuable trade-offs for decision-markers for complex system design.
  arXiv  Detail & Related papers  (2023-09-11T14:29:47Z)
• A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences [55.25331349436895]
  Deep generative models have emerged as a popular machine learning-based approach for inverse problems in the life sciences. These problems often require sampling new designs that satisfy multiple properties of interest in addition to learning the data distribution.
  arXiv  Detail & Related papers  (2022-10-19T19:04:45Z)
• Bayesian Optimization For Multi-Objective Mixed-Variable Problems [0.0]
  We present MixMOBO, the first mixed variable multi-objective Bayesian optimization framework for such problems. We also present HedgeMO, a modified Hedge strategy that uses a portfolio of acquisition functions in multi-objective problems. We show that MixMOBO performs well against other mixed-variable algorithms on synthetic problems.
  arXiv  Detail & Related papers  (2022-01-30T09:38:06Z)
• Multi-Objective Constrained Optimization for Energy Applications via Tree Ensembles [55.23285485923913]
  Energy systems optimization problems are complex due to strongly non-linear system behavior and multiple competing objectives. In some cases, proposed optimal solutions need to obey explicit input constraints related to physical properties or safety-critical operating conditions. This paper proposes a novel data-driven strategy using tree ensembles for constrained multi-objective optimization of black-box problems.
  arXiv  Detail & Related papers  (2021-11-04T20:18:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.