Transfer Learning in Bayesian Optimization for Aircraft Design

• URL: http://arxiv.org/abs/2603.28999v1
• Date: Mon, 30 Mar 2026 21:01:47 GMT
• Title: Transfer Learning in Bayesian Optimization for Aircraft Design
• Abstract summary: We present a method that leverages an ensemble of surrogate models using transfer learning and integrates it in a constrained Bayesian optimization framework.<n>We identify challenges particular to aircraft design optimization related to heterogeneous design variables and constraints.<n>Results show significant improvement in convergence in early optimization iterations compared to standard Bayesian optimization.
• Score: 0.34998703934432673
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The use of transfer learning within Bayesian optimization addresses the disadvantages of the so-called \textit{cold start} problem by using source data to aid in the optimization of a target problem. We present a method that leverages an ensemble of surrogate models using transfer learning and integrates it in a constrained Bayesian optimization framework. We identify challenges particular to aircraft design optimization related to heterogeneous design variables and constraints. We propose the use of a partial-least-squares dimension reduction algorithm to address design space heterogeneity, and a \textit{meta} data surrogate selection method to address constraint heterogeneity. Numerical benchmark problems and an aircraft conceptual design optimization problem are used to demonstrate the proposed methods. Results show significant improvement in convergence in early optimization iterations compared to standard Bayesian optimization, with improved prediction accuracy for both objective and constraint surrogate models.

Related papers

• Preference-Guided Diffusion for Multi-Objective Offline Optimization [64.08326521234228]
  We propose a preference-guided diffusion model for offline multi-objective optimization.<n>Our guidance is a preference model trained to predict the probability that one design dominates another.<n>Our results highlight the effectiveness of classifier-guided diffusion models in generating diverse and high-quality solutions.
  arXiv  Detail & Related papers  (2025-03-21T16:49:38Z)
• A Fenchel-Young Loss Approach to Data-Driven Inverse Optimization [1.7068557927955381]
  We build a connection between inverse optimization and the Fenchel-Young (FY) loss originally designed for structured prediction.<n>This new approach is amenable to efficient gradient-based optimization, hence much more efficient than existing methods.
  arXiv  Detail & Related papers  (2025-02-22T07:04:32Z)
• Learning Joint Models of Prediction and Optimization [56.04498536842065]
  Predict-Then-Then framework uses machine learning models to predict unknown parameters of an optimization problem from features before solving. This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by joint predictive models.
  arXiv  Detail & Related papers  (2024-09-07T19:52:14Z)
• End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty [55.04219793298687]
  The Predict-Then-Forecast (PtO) paradigm in machine learning aims to maximize downstream decision quality. This paper extends the PtO methodology to optimization problems with nondifferentiable Ordered Weighted Averaging (OWA) objectives. It shows how optimization of OWA functions can be effectively integrated with parametric prediction for fair and robust optimization under uncertainty.
  arXiv  Detail & Related papers  (2024-02-12T16:33:35Z)
• Generative Adversarial Model-Based Optimization via Source Critic Regularization [25.19579059511105]
  We propose generative adversarial model-based optimization using adaptive source critic regularization (aSCR) ASCR constrains the optimization trajectory to regions of the design space where the surrogate function is reliable. We show how leveraging aSCR with standard Bayesian optimization outperforms existing methods on a suite of offline generative design tasks.
  arXiv  Detail & Related papers  (2024-02-09T16:43:57Z)
• Functional Graphical Models: Structure Enables Offline Data-Driven Optimization [111.28605744661638]
  We show how structure can enable sample-efficient data-driven optimization. We also present a data-driven optimization algorithm that infers the FGM structure itself.
  arXiv  Detail & Related papers  (2024-01-08T22:33:14Z)
• 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)
• Backpropagation of Unrolled Solvers with Folded Optimization [55.04219793298687]
  The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. One typical strategy is algorithm unrolling, which relies on automatic differentiation through the operations of an iterative solver. This paper provides theoretical insights into the backward pass of unrolled optimization, leading to a system for generating efficiently solvable analytical models of backpropagation.
  arXiv  Detail & Related papers  (2023-01-28T01:50:42Z)
• Optimal Design of Electric Machine with Efficient Handling of Constraints and Surrogate Assistance [5.387300498478744]
  This article proposes an optimization method incorporated into a popularly-used evolutionary multi-objective optimization algorithm - NSGA-II. The proposed method exploits the inexpensiveness of geometric constraints to generate feasible designs by using a custom repair operator.
  arXiv  Detail & Related papers  (2022-06-03T17:13:29Z)
• Robust Topology Optimization Using Multi-Fidelity Variational Autoencoders [1.0124625066746595]
  A robust topology optimization (RTO) problem identifies a design with the best average performance. A neural network method is proposed that offers computational efficiency. Numerical application of the method is shown on the robust design of L-bracket structure with single point load as well as multiple point loads.
  arXiv  Detail & Related papers  (2021-07-19T20:40:51Z)
• Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification [0.0]
  We present a novel adaptive optimization algorithm for black-box multi-objective optimization problems with binary constraints. Our method is based on probabilistic regression and classification models, which act as a surrogate for the optimization goals. We also present a novel ellipsoid truncation method to speed up the expected hypervolume calculation.
  arXiv  Detail & Related papers  (2020-08-27T09:15:02Z)
• Automatically Learning Compact Quality-aware Surrogates for Optimization Problems [55.94450542785096]
  Solving optimization problems with unknown parameters requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values. Recent work has shown that including the optimization problem as a layer in a complex training model pipeline results in predictions of iteration of unobserved decision making. We show that we can improve solution quality by learning a low-dimensional surrogate model of a large optimization problem.
  arXiv  Detail & Related papers  (2020-06-18T19:11:54Z)

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.