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 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

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

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

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

arXiv Machine Learning

Jul 23

Generative AI-enhanced Probabilistic Multi-Fidelity Surrogate Modeling Via Transfer Learning

arXiv:2602. 00072v2 Announce Type: replace Abstract: The performance of machine learning surrogates is critically dependent on data quality and quantity.

By Jice Zeng, David Barajas-Solano, Hui Chen

fine-tuning benchmarks data-engineering

arXiv AI

Jun 10

Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training

arXiv:2606. 09949v1 Announce Type: cross Abstract: Data-driven PDE surrogates are trained with data produced by numerical PDE solvers.

By Pierre Cesar (DATAMOVE), Sofya Dymchenko (DATAMOVE), Abhishek Purandare (DATAMOVE), Bruno Raffin (DATAMOVE)

diffusion

arXiv Machine Learning

Aug 3

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery

arXiv:2607. 29225v1 Announce Type: new Abstract: Bayesian Optimization (BO) is widely adopted for data-efficient optimization in scientific and engineering applications, yet its computational cost is rarely evaluated alongside optimization performance.

By Panagiotis Krokidas, Christoforos Rekatsinas, Vassilis Sioros, Grigorios M. Chatziathanasiou, Efi-Maria Papia, George Giannakopoulos

robotics benchmarks

arXiv Machine Learning

Jun 10

Uncertainty-aware Multi-fidelity Closure via Conditional Normalizing Flows

arXiv:2606. 09857v1 Announce Type: new Abstract: Reduced-order models (ROMs) provide an efficient surrogate for complex multiscale systems, but their predictive accuracy is often compromised by truncation errors and the inadequate representation of interactions between resolved and unresolved scales.

By Jice Zeng, Shady E. Ahmed, David Barajas-Solano, Panos Stinis

arXiv AI

Jun 17

ZIVARI-TLBO: A Zero-Cost Inter-Group Evaluated-Elite Relay Mechanism for Teaching-Learning-Based Optimization

arXiv:2606. 17087v1 Announce Type: cross Abstract: ZIVARI-TLBO is a grouped Teaching-Learning-Based Optimization (TLBO) method that augments an existing population-state controller with a fixed inter-group evaluated-elite relay.

By Pezhman Zivari

benchmarks

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