Generative machine learning provides an end-to-end framework for aerodynamic design optimization by generating diverse aerodynamic configurations conditioned on target performance. However, the genera...
arXiv:2604. 09434v3 Announce Type: replace-cross Abstract: Turbulent boundary layers over aerodynamic surfaces are a major source of aircraft drag, yet their control remains challenging due to multiscale dynamics and spatial variability, particularly under adverse pressure gradients.
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...
The paper presents a dataset of roughly 10,000 RANS simulations of steady, incompressible, two‑dimensional subsonic flow around downforce‑generating NACA 4‑digit airfoils, aimed at automotive and motorsport regimes. It explores geometry‑conditioned neural surrogates—comparing neural fields, neural ODEs, MLPs, graph models, and spectral conditioning—and introduces Airfoil2Vec, a spectral geometry encoder that fuses joint contour, camber, and thickness spectra to predict continuous pressure and velocity fields. The surrogate models are evaluated on angle‑of‑attack interpolation, unseen NACA geometries, and non‑NACA airfoils, achieving accurate aerodynamic predictions with orders‑of‑magnitude speedups over traditional CFD.
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:2606. 25761v1 Announce Type: new Abstract: When gradient information is unavailable, black-box optimization (BBO) methods provide a practical alternative.
The paper reports the first in‑space demonstration of using machine learning‑based warm starts to accelerate trajectory optimization for the Astrobee free‑flying robot on the International Space Station. A neural network is trained to learn the structure of the trajectory generation problem solved by sequential convex programming, and onboard predictions reduce solver iterations by 60% for rotational dynamics and 50% for obstacle‑laden scenarios. This approach enables real‑time trajectory planning while respecting safety constraints on limited flight hardware.
FALCON‑S is a modular, high‑fidelity simulator designed for fixed‑wing aerial robots operating near the ground. It models full 6DoF rigid‑body physics, semi‑empirical ground‑effect aerodynamics, actuator dynamics, sensor noise, and environmental disturbances, and supports both CPU and GPU backends via Torch and NVIDIA Warp for large‑scale reinforcement learning and optimal control. The framework offers a unified interface for various controllers, including RL and optical control algorithms, and allows cross‑validation with X‑Plane and JSBSim for engineering integration and visual fidelity.
arXiv:2505. 18201v2 Announce Type: replace-cross Abstract: Controlling flapping-wing drones requires controllers that handle time-varying, nonlinear, underactuated dynamics from incomplete, noisy sensor data.
The paper presents a method for learning compact, expressive parameterisations for Bayesian shape optimisation by applying principal component analysis to deformations between existing designs. This approach yields a linear, interpretable search space where the number of components balances expressivity and dimensionality. Experiments on aerofoils, wings, and radio‑frequency cavities demonstrate improved sample efficiency and exploration beyond hand‑crafted baselines.
arXiv:2606. 00702v1 Announce Type: cross Abstract: We propose to turn generalist multi-embodiment value functions into reusable models for robot design.
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.