cfd

GeoWind2Plan 是一个面向城市低空无人机的几何-风场-规划框架，利用局部几何条件神经算子在约 3 秒内预测任务相关的 3D 城市风场（CFD 需约 8 小时），并据此优化能量高效的 3D 路径与速度剖面，相比无风感知规划可减少 4.5%–12.7% 的能耗。

This paper presents a novel approach combining Graph Neural Networks with augmented Neural Ordinary Differential Equations (GNODE) for stable and accurate spatio-temporal prediction of unsteady airfoil aerodynamics, outperforming autoregressive baselines on transonic shock and non-linear dynamics tests.

Ventorah is a browser-based tool that allows users to run virtual wind tunnel aerodynamic analysis directly in their web browser.

GM is leveraging AI and machine learning to collapse traditional engineering phases into a single probabilistic method, reducing simulation times from hours to minutes and accelerating vehicle development.

This paper investigates the role of group-equivariant architectures in neural fluid dynamics surrogates, introducing the AB-GATr model. It finds that equivariance is beneficial when data lacks strong alignment, but can degrade performance on highly aligned datasets.

This paper presents AI CFD Scientist, an open-source AI agent for computational fluid dynamics that autonomously discovers physics corrections using vision-language verification and code modification, outperforming general AI scientists on CFD tasks.

This paper introduces AeroJEPA, a Joint-Embedding Predictive Architecture for scalable 3D aerodynamic field modeling. It addresses limitations in current surrogate models by predicting semantic latent representations of flow fields, enabling efficient high-fidelity analysis and design optimization.