Live
Black Hat USADark ReadingBlack Hat AsiaAI Business🚀 Build a Professional Image Converter GUI in Python (Step-by-Step)DEV CommunityClaude Code Hooks: How to Auto-Format, Lint, and Test on Every SaveDev.to AIFunctional Emotions in Large Language Models: What Anthropic Found Inside ClaudeMedium AIWhy Nobody Is Testing AI Agent Security at Scale — And How Swarm Simulation Could Change ThatDev.to AIThe 10 Claude “Plugins” You Actually Need in 2026Medium AIHow AI Is Changing the Way We Build Online BusinessesDev.to AI5 Patterns for Building Resilient Event-Driven IntegrationsDEV CommunityAGI Won’t Automate Most Jobs—Economist Reveals Why They’re Not Worth ItDev.to AIThe AI Agent's Guide to Building a Writing PortfolioDev.to AIMy Claude Code Buddy Moved Into My MacBook's Notch and I Can't Stop Looking at ItDEV CommunityChoosing an AI Agent Orchestrator in 2026: A Practical ComparisonDev.to AII Turned My MacBook's Notch Into a Control Center for AI Coding AgentsDEV CommunityBlack Hat USADark ReadingBlack Hat AsiaAI Business🚀 Build a Professional Image Converter GUI in Python (Step-by-Step)DEV CommunityClaude Code Hooks: How to Auto-Format, Lint, and Test on Every SaveDev.to AIFunctional Emotions in Large Language Models: What Anthropic Found Inside ClaudeMedium AIWhy Nobody Is Testing AI Agent Security at Scale — And How Swarm Simulation Could Change ThatDev.to AIThe 10 Claude “Plugins” You Actually Need in 2026Medium AIHow AI Is Changing the Way We Build Online BusinessesDev.to AI5 Patterns for Building Resilient Event-Driven IntegrationsDEV CommunityAGI Won’t Automate Most Jobs—Economist Reveals Why They’re Not Worth ItDev.to AIThe AI Agent's Guide to Building a Writing PortfolioDev.to AIMy Claude Code Buddy Moved Into My MacBook's Notch and I Can't Stop Looking at ItDEV CommunityChoosing an AI Agent Orchestrator in 2026: A Practical ComparisonDev.to AII Turned My MacBook's Notch Into a Control Center for AI Coding AgentsDEV Community
AI NEWS HUBbyEIGENVECTOREigenvector

Multi-fidelity approaches for general constrained Bayesian optimization with application to aircraft design

arXiv stat.MLby Oihan Cordelier, Youssef Diouane, Nathalie Bartoli, Eric LaurendeauApril 1, 20262 min read0 views
Source Quiz

arXiv:2603.28987v1 Announce Type: cross Abstract: Aircraft design relies heavily on solving challenging and computationally expensive Multidisciplinary Design Optimization problems. In this context, there has been growing interest in multi-fidelity models for Bayesian optimization to improve the MDO process by balancing computational cost and accuracy through the combination of high- and low-fidelity simulation models, enabling efficient exploration of the design process at a minimal computational effort. In the existing literature, fidelity selection focuses only on the objective function to decide how to integrate multiple fidelity levels, balancing precision and computational cost using variance reduction criteria. In this work, we propose novel multi-fidelity selection strategies. Spec

View PDF HTML (experimental)

Abstract:Aircraft design relies heavily on solving challenging and computationally expensive Multidisciplinary Design Optimization problems. In this context, there has been growing interest in multi-fidelity models for Bayesian optimization to improve the MDO process by balancing computational cost and accuracy through the combination of high- and low-fidelity simulation models, enabling efficient exploration of the design process at a minimal computational effort. In the existing literature, fidelity selection focuses only on the objective function to decide how to integrate multiple fidelity levels, balancing precision and computational cost using variance reduction criteria. In this work, we propose novel multi-fidelity selection strategies. Specifically, we demonstrate how incorporating information from both the objective and the constraints can further reduce computational costs without compromising the optimality of the solution. We validate the proposed multi-fidelity optimization strategy by applying it to four analytical test cases, showcasing its effectiveness. The proposed method is used to efficiently solve a challenging aircraft wing aero-structural design problem. The proposed setting uses a linear vortex lattice method and a finite element method for the aerodynamic and structural analysis respectively. We show that employing our proposed multi-fidelity approach leads to $86%$ to $200%$ more constraint compliant solutions given a limited budget compared to the state-of-the-art approach.

Subjects:

Optimization and Control (math.OC); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (stat.ML)

Cite as: arXiv:2603.28987 [math.OC]

(or arXiv:2603.28987v1 [math.OC] for this version)

https://doi.org/10.48550/arXiv.2603.28987

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Youssef Diouane [view email] [v1] Mon, 30 Mar 2026 20:36:13 UTC (1,061 KB)

Was this article helpful?

Sign in to highlight and annotate this article

AI
Ask AI about this article
Powered by Eigenvector · full article context loaded
Ready

Conversation starters

Ask anything about this article…

Daily AI Digest

Get the top 5 AI stories delivered to your inbox every morning.

More about

modelannounceapplication

Knowledge Map

Knowledge Map
TopicsEntitiesSource
Multi-fidel…modelannounceapplicationanalysisarxivarXiv stat.…

Connected Articles — Knowledge Graph

This article is connected to other articles through shared AI topics and tags.

Knowledge Graph100 articles · 233 connections
Scroll to zoom · drag to pan · click to open

Discussion

Sign in to join the discussion

No comments yet — be the first to share your thoughts!

More in Products