A Neural Surrogate-Enhanced Multi-Method Framework for Robust Wing Design Optimization

• URL: http://arxiv.org/abs/2510.08582v1
• Date: Mon, 29 Sep 2025 17:26:37 GMT
• Title: A Neural Surrogate-Enhanced Multi-Method Framework for Robust Wing Design Optimization
• Authors: Arash Fath Lipaei, AmirHossein Ghaemi, Melika Sabzikari,
• Abstract summary: This paper introduces a modular and scalable design optimization framework for the wing design process.<n>The pipeline starts with the generation of initial wing geometries and then proceeds to optimize the wing using several algorithms.<n>Results show improvement in aerodynamic qualities and robust stability properties, offering a mechanism for wing design at speed and precision.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces a modular and scalable design optimization framework for the wing design process that enables faster early-phase design while ensuring aerodynamic stability. The pipeline starts with the generation of initial wing geometries and then proceeds to optimize the wing using several algorithms. Aerodynamic performance is assessed using a Vortex Lattice Method (VLM) applied to a carefully selected dataset of wing configurations. These results are employed to develop surrogate neural network models, which can predict lift and drag rapidly and accurately. The stability evaluation is implemented by setting the control surfaces and components to fixed positions in order to have realistic flight dynamics. The approach unifies and compares several optimization techniques, including Particle Swarm Optimization (PSO), Genetic Algorithms (GA), gradient-based MultiStart methods, Bayesian optimization, and Lipschitz optimization. Each method ensures constraint management via adaptive strategies and penalty functions, where the targets for lift and design feasibility are enforced. The progression of aerodynamic characteristics and geometries over the optimization iterations will be investigated in order to clarify each algorithm's convergence characteristics and performance efficiency. Our results show improvement in aerodynamic qualities and robust stability properties, offering a mechanism for wing design at speed and precision. In the interest of reproducibility and community development, the complete implementation is publicly available at https://github.com/AmirHosseinGhaemi2000/CHIMERA.

Related papers

• Optimization and Generation in Aerodynamics Inverse Design [41.756961128464816]
  Inverse design with physics-based objectives is challenging because it couples high-dimensional geometry with expensive simulations.<n>We revisit inverse design through two canonical solutions, the optimal design point and the optimal design distribution.<n>We propose a new training loss for cost predictors and a density-gradient optimization method that improves objectives while preserving plausible shapes.
  arXiv  Detail & Related papers  (2026-02-03T14:32:26Z)
• Riemannian Lyapunov Optimizer: A Unified Framework for Optimization [6.493476506951333]
  We introduce a family of optimization algorithms that unifies classic algorithms within one geometric framework.<n>RLOs bridge control theory and modern machine learning optimization, providing a unified language and a systematic toolkit.
  arXiv  Detail & Related papers  (2026-01-29T20:00:25Z)
• Optimizing Optimizers for Fast Gradient-Based Learning [53.81268610971847]
  We lay the theoretical foundation for automating design in gradient-based learning.<n>By treating gradient loss signals as a function that translates to parameter motions, the problem reduces to a family of convex optimization problems.
  arXiv  Detail & Related papers  (2025-12-06T09:50:41Z)
• TripOptimizer: Generative 3D Shape Optimization and Drag Prediction using Triplane VAE Networks [4.4288915456583755]
  Tripr is a framework for rapid aerodynamic analysis and shape optimization directly from vehicle point cloud data.<n>Tripr employs a Variational Autoencoder featuring a triplane-based implicit neural representation for high-fidelity 3D geometry reconstruction and a drag coefficient prediction head.
  arXiv  Detail & Related papers  (2025-09-05T15:14:19Z)
• Adjoint-Based Aerodynamic Shape Optimization with a Manifold Constraint Learned by Diffusion Models [12.019764781438603]
  We introduce an adjoint-based aerodynamic shape optimization framework that integrates a diffusion model trained on existing designs to learn a smooth manifold of aerodynamically viable shapes.<n>We demonstrate how AI generated priors integrates effectively with adjoint methods to enable robust, high-fidelity aerodynamic shape optimization through automatic differentiation.
  arXiv  Detail & Related papers  (2025-07-31T11:21:20Z)
• Divergence Minimization Preference Optimization for Diffusion Model Alignment [66.31417479052774]
  Divergence Minimization Preference Optimization (DMPO) is a principled method for aligning diffusion models by minimizing reverse KL divergence.<n>DMPO can consistently outperform or match existing techniques across different base models and test sets.
  arXiv  Detail & Related papers  (2025-07-10T07:57:30Z)
• Efficient Design of Compliant Mechanisms Using Multi-Objective Optimization [50.24983453990065]
  We address the synthesis of a compliant cross-hinge mechanism capable of large angular strokes.<n>We formulate a multi-objective optimization problem based on kinetostatic performance measures.
  arXiv  Detail & Related papers  (2025-04-23T06:29:10Z)
• Accelerated Gradient-based Design Optimization Via Differentiable Physics-Informed Neural Operator: A Composites Autoclave Processing Case Study [0.0]
  We introduce a novel Physics-Informed DeepONet (PIDON) architecture to effectively model the nonlinear behavior of complex engineering systems.<n>We demonstrate the effectiveness of this framework in the optimization of aerospace-grade composites curing processes achieving a 3x speedup.<n>The proposed model has the potential to be used as a scalable and efficient optimization tool for broader applications in advanced engineering and digital twin systems.
  arXiv  Detail & Related papers  (2025-02-17T07:11:46Z)
• FADAS: Towards Federated Adaptive Asynchronous Optimization [56.09666452175333]
  Federated learning (FL) has emerged as a widely adopted training paradigm for privacy-preserving machine learning. This paper introduces federated adaptive asynchronous optimization, named FADAS, a novel method that incorporates asynchronous updates into adaptive federated optimization with provable guarantees. We rigorously establish the convergence rate of the proposed algorithms and empirical results demonstrate the superior performance of FADAS over other asynchronous FL baselines.
  arXiv  Detail & Related papers  (2024-07-25T20:02:57Z)
• A mechanism-driven reinforcement learning framework for shape optimization of airfoils [0.32885740436059047]
  A novel mechanism-driven reinforcement learning framework is proposed for airfoil shape optimization. An efficient solver for steady equations is employed in the reinforcement learning method. A neural network architecture is designed based on an attention mechanism to make the learning process more sensitive to the minor change of the airfoil geometry.
  arXiv  Detail & Related papers  (2024-03-07T08:48:42Z)
• DEBOSH: Deep Bayesian Shape Optimization [48.80431740983095]
  We propose a novel uncertainty-based method tailored to shape optimization. It enables effective BO and increases the quality of the resulting shapes beyond that of state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-09-28T11:01:42Z)
• SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients [99.13839450032408]
  It is desired to design a universal framework for adaptive algorithms to solve general problems. In particular, our novel framework provides adaptive methods under non convergence support for setting.
  arXiv  Detail & Related papers  (2021-06-15T15:16:28Z)
• Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization [0.0]
  Adjoint-based optimization methods are attractive for aerodynamic shape design. They can become prohibitively expensive when multiple optimization problems are being solved. We propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver.
  arXiv  Detail & Related papers  (2020-08-15T15:09:21Z)
• EOS: a Parallel, Self-Adaptive, Multi-Population Evolutionary Algorithm for Constrained Global Optimization [68.8204255655161]
  EOS is a global optimization algorithm for constrained and unconstrained problems of real-valued variables. It implements a number of improvements to the well-known Differential Evolution (DE) algorithm. Results prove that EOSis capable of achieving increased performance compared to state-of-the-art single-population self-adaptive DE algorithms.
  arXiv  Detail & Related papers  (2020-07-09T10:19:22Z)

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.