Hugging Face Trending Papers July 13, 2026

A multi-scale feature enhanced graph neural network for fluid dynamics prediction in complex geometries

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Industrial design in fields such as vehicle and aerospace engineering often relies on large-scale numerical simulations to evaluate fluid dynamics performance, which can incur substantial computational costs. Deep neural networks have shown promise in improving simulation efficiency, especially graph neural networks (GNNs), which demonstrate great potential due to their flexibility with unstructured data.

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benchmarks

arXiv Machine Learning

Jul 14

A multi-scale feature enhanced graph neural network for fluid dynamics prediction in complex geometries

arXiv:2607. 11672v1 Announce Type: new Abstract: Industrial design in fields such as vehicle and aerospace engineering often relies on large-scale numerical simulations to evaluate fluid dynamics performance, which can incur substantial computational costs.

By Li Xiao, Tianyu Li, Yiye Zou, Mingjie Zhang, Xiaogangd Deng

benchmarks

arXiv Machine Learning

2d ago

HI-MeshGraphNets: Efficient and Accurate Mesh-based Physics Learning with Hierarchical Multi-scale Graph Neural Networks

arXiv:2608. 13827v1 Announce Type: new Abstract: Machine-learned physical surrogate models have become promising alternatives to mesh-based numerical solvers.

By SiHun Lee, Dong-Hyuk Park, Taesoo Bang, Seung-Hoon Kang

benchmarks

arXiv Machine Learning

Jun 2

Graph Navier Stokes Networks

arXiv:2605. 21247v3 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have emerged as a cornerstone of deep learning, with most existing methods rooted in graph signal processing and diffusion equations to model message passing.

By Zexing Zhao, Guangsi Shi, Yu Gong, Tianyu Wang, Shirui Pan, Hongye Cheng, Yuxiao Li

diffusion benchmarks

arXiv Machine Learning

Jun 16

Smoothness Errors in Dynamics Models and How to Avoid Them

arXiv:2602. 05352v3 Announce Type: replace Abstract: Modern neural networks have shown promise for solving partial differential equations over surfaces, often by discretizing the surface as a mesh and learning with a mesh-aware graph neural network.

By Edward Berman, Luisa Li, Jung Yeon Park, Robin Walters

llms diffusion

arXiv Machine Learning

Jun 9

A Geometry-Aware Triplane Field Network for Vehicle Aerodynamic Prediction

arXiv:2606. 07724v1 Announce Type: new Abstract: High-fidelity computational fluid dynamics (CFD) is crucial to vehicle aerodynamic analysis, but its cost still constrains early-stage design exploration.

By Kangkang Qi, Huiyu Yang, Keqi Ding, Yunpeng Wang, Yuntian Chen, Yuanwei Bin, Rikui Zhang, Jianchun Wang

benchmarks

arXiv AI

Aug 10

Fluid-DiT: Graph-Free Diffusion Transformers for Fluid Flow Simulations Learning

arXiv:2608. 07161v1 Announce Type: cross Abstract: Simulating complex fluid flows requires capturing full equilibrium distributions rather than just mean trajectories, yet high-fidelity solvers remain computationally prohibitive.

By Shentong Mo, Guolin Ke

llms diffusion benchmarks

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