Surrogate-Assisted Optimization

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11 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Optimization-Embedded Active Multi-Fidelity Surrogate Learning for Multi-Condition Airfoil Shape Optimization
Mar 17, 2026Isaac Robledo, Alberto Vilariño, Arnau Miró +3Multi-Fidelity Optimization Surrogate Modeling
Aerial Platforms Department Subdirectorate General for Aeronautical Systems Spanish National Institute for Aerospace Technology (INTA) · Department of Aerospace Engineering Universidad Carlos III de Madrid Madrid, Spain · TUAREG Research Group Universitat Politècnica de Catalunya (UPC) CASE Large-Scale Computational Fluid Dynamics Barcelona Supercomputing Centre (BSC) Barcelona, Spain
Local Constrained Bayesian Optimization
Mar 9, 2026Jing Jingzhe, Fan Zheyi, Szu Hui Ng +1Surrogate-Assisted Optimization Bayesian Optimization
State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China · School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China · Department of Industrial Systems Engineering & Management, National University of Singapore, Singapore.
APEX: Approximate-but-exhaustive search for ultra-large combinatorial synthesis libraries
Oct 28, 2025Aryan Pedawi, Jordi Silvestre-Ryan, Bradley Worley +5Surrogate-Assisted Optimization AI-Assisted Scientific Research
Work performed at Numerion Labs · NVIDIA

Mar 17, 2026, physics.flu-dynJ/K move · Enter open · S save

Optimization-Embedded Active Multi-Fidelity Surrogate Learning for Multi-Condition Airfoil Shape Optimization

Isaac Robledo, Alberto Vilariño, Arnau Miró, Oriol Lehmkuhl+2

Aerial Platforms Department Subdirectorate General for Aeronautical Systems Spanish National Institute for Aerospace Technology (INTA) · Department of Aerospace Engineering Universidad Carlos III de Madrid Madrid, Spain · TUAREG Research Group Universitat Politècnica de Catalunya (UPC) CASE Large-Scale Computational Fluid Dynamics Barcelona Supercomputing Centre (BSC) Barcelona, Spain

Multi-Fidelity Optimization Surrogate Modeling Surrogate-Assisted Optimization Active Learning Multifidelity Modeling

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Active multi-fidelity surrogate modeling is developed for multi-condition airfoil shape optimization to reduce high-fidelity CFD cost while retaining RANS-consistent aerodynamic metrics. The framework couples a low-fidelity-informed Gaussian process regression transfer model with uncertainty-triggered sampling and a synchronized elitism rule embedded in a hybrid genetic algorithm. Low-fidelity XFOIL evaluations provide inexpensive features, while sparse RANS simulations are adaptively allocated when predictive uncertainty exceeds a threshold; elite candidates are mandatorily validated at high fidelity, and the population is re-evaluated to prevent evolutionary selection based on outdated fitness values produced by earlier surrogate states. The method is demonstrated for a two-point problem at

Re=6\times10^6

with cruise at

α=2^\circ

(maximize

E=L/D

) and take-off at

α=10^\circ

(maximize

C_L

) using a 12-parameter CST representation. Independent multi-fidelity surrogates per flight condition enable decoupled refinement. The optimized design improves cruise efficiency by 41.05% and take-off lift by 20.75% relative to the best first-generation individual. Over the full campaign, RANS evaluations were required for only 14.78% and 9.5% of the condition-specific candidate evaluations at cruise and take-off, respectively. These percentages quantify the reduction in high-fidelity usage relative to the fixed automated RANS workflow adopted as the high-fidelity reference in this study.

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Related topics

Latest papers 63

Optimization-Embedded Active Multi-Fidelity Surrogate Learning for Multi-Condition Airfoil Shape Optimization

Local Constrained Bayesian Optimization

APEX: Approximate-but-exhaustive search for ultra-large combinatorial synthesis libraries