arXiv Machine Learning

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

arXiv:2603. 17057v2 Announce Type: replace-cross Abstract: 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.

arXiv Machine Learning

Sep 7

A Data Fusion Framework for Grounding Aerospace Surrogate Model via Experimental Wind-Tunnel Observations

The paper introduces a correction framework that grounds a CFD-trained deep learning surrogate model for aerospace aerodynamics using wind‑tunnel pressure‑sensor (PSP) data. By training a correction network on spatially registered PSP measurements at two Mach numbers, the authors adjust the surrogate’s predictions without retraining its core parameters, achieving improved agreement with experimental pressure distributions—especially at the wing suction peak and shock location. The grounded surrogate matches measurements within 2.3–2.7% of the Cp range on unseen angles of attack and outperforms simple interpolation between measured states.

By Nitin Nagesh Kulkarni, Dheeraj Vemula, Yin Yu, Peter Lyu, Juan J. Alonso

arXiv AI

Jul 14

Knowledge-Constrained Shape Optimization with a Mixture-of-Experts Neural Operator for High-Confidence Design

arXiv:2607. 09763v1 Announce Type: cross Abstract: Engineering shape optimization faces challenges in both expert-dependent problem setup and surrogate-model reliability.

By Wenhao Fan, Yuanwei Bin, Jianghan Gu, Wenfa Luo, Jiao Xiang, Yuntian Chen, Shiyi Chen

arXiv Machine Learning

Jun 30

A Bayesian latent Gaussian process framework for aerodynamic uncertainty quantification

arXiv:2606. 28871v1 Announce Type: cross Abstract: Predicting the aerodynamic performance (e.

By Geoffrey Davis, Ashwin Renganathan

arXiv Machine Learning

Sep 16

Neural Field Ensembles for Aerodynamic Surface Prediction: Winning Solution to the ONERA CRM Wall Distribution 2025 Challenge

arXiv:2609.17160v1 Announce Type: new Abstract: Machine-learning surrogate models offer a promising alternative to high-fidelity Computational Fluid Dynamics (CFD) simulations for aerodynamic analysi...

By Lionel Salesses, Caroline Sainvitu, Tariq Benamara

benchmarks

arXiv Machine Learning

Jun 15

Multi-fidelity aerodynamic data fusion by autoencoder transfer learning

arXiv:2512. 13069v2 Announce Type: replace Abstract: Accurate aerodynamic prediction often relies on high-fidelity simulations; however, their prohibitive computational costs severely limit their applicability in data-driven modeling.

By Javier Nieto-Centenero, Esther Andr\'es, Rodrigo Castellanos

rag fine-tuning

arXiv Machine Learning

Jun 18

Reinforcement Learning for Accelerated Aerodynamic Shape Optimisation

arXiv:2507. 17786v2 Announce Type: replace Abstract: We introduce a reinforcement learning (RL) based adaptive optimization algorithm for aerodynamic shape optimization focused on dimensionality reduction.

By Florian Sobieczky, Alfredo Lopez, Erika Dudkin, Christopher Lackner, Matthias Hochsteger, Bernhard Scheichl, Helmut Sobieczky

reinforcement-learning

arXiv Machine Learning

2d ago

SHIFT-Truck: A High-Fidelity Aerodynamics Dataset and Benchmark for Pickup Trucks

arXiv:2609.38638v1 Announce Type: cross Abstract: Pickup trucks account for 14% of new light-duty vehicles produced in the United States, yet are among the least aerodynamic. Their open cargo bed add...

By Riddhiman Raut, Yin Yu, Aashwin Anand Mishra, Michael Emory, Thomas Economon, Peter Lyu, Juan J. Alonso

benchmarks

arXiv Machine Learning

2d ago

Airfoil2Vec: Spectral Geometry-Conditioned Neural Surrogate Models for Airfoil Aerodynamics and a Downforce-Generating CFD Dataset

arXiv:2609.38213v1 Announce Type: cross Abstract: We introduce a dataset of approximately 10,000 Reynolds-Averaged Navier-Stokes (RANS) simulations of steady, incompressible, two-dimensional subsonic...

By Haitz S\'aez de Oc\'ariz Borde, Flavio Savarino, Andrei Cristian Popescu, Pietro Innocenzi, Pantelis Papageorgiou, Xerxes Xian Chong

arXiv Machine Learning

Jun 25

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data

arXiv:2603. 22050v2 Announce Type: replace-cross Abstract: Supervised machine learning describes the practice of fitting a parameterized model to labeled input-output data.

By Atticus Rex, Elizabeth Qian, David Peterson

arXiv Machine Learning

Sep 18

How Does Distribution Shift Shape Pretraining Gains in Neural PDE Surrogates?

The study investigates how distribution shift influences the benefits of pretraining neural PDE surrogates for computational fluid dynamics. Researchers pretrained a model on 254,909 RANS solutions from one airfoil family and fine‑tuned it on a new family under two target settings—identical Spalart‑Allmaras (SA) modeling and SA with added $e^N$ transition modeling—while keeping freestream ranges matched. Results show that at 1,000 fine‑tuning samples, the pretrained model matches a from‑scratch model trained on 3.25× more data for the same‑SA target and 2.58× more for the transition‑modeled target; by 5,000 samples the advantage reverses. Additionally, increasing the number of distinct airfoils in the fine‑tuning set reduces error for both targets, but the improvement is significant only for the same‑SA case.

By Pochinapeddi Sai Bhargav, Nithin Somasekharan, Rohit Sunil Kanchi, Sicheng He, Shaowu Pan

fine-tuning

arXiv Machine Learning

Jul 28

Transfer Learning Architectures for Scalable Multi-Fidelity Bayesian Optimization

arXiv:2607. 23404v1 Announce Type: new Abstract: Self-driving laboratories increasingly rely on multi-fidelity Bayesian optimization (MFBO) to balance cheap, approximate evaluations against scarce, expensive ones, with a predictive surrogate at its core.

By Jaewook Lee, Ethan Errington, Christian D. Lorenz, Miao Guo

robotics fine-tuning benchmarks

arXiv Machine Learning

Jun 2

Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA

arXiv:2606. 00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems.

By Xiao Jin, Yongxiong Wang, Haobo Liu, Yudong Du, Yukun Du

reinforcement-learning benchmarks

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