UniField: Joint Multi-Domain Training for Universal Surface Pressure Modeling

TL;DR

UniField is a unified neural network framework that jointly trains across multiple aerodynamic domains to improve surface pressure prediction accuracy, especially in data-scarce scenarios, by leveraging shared representations and diverse datasets.

Contribution

The paper introduces UniField, a novel multi-domain training framework with a shared encoder and domain-specific layers, and a large-scale CFD dataset ThingiCFD, advancing universal aerodynamic surface pressure modeling.

Findings

Achieves state-of-the-art results on DrivAerNet++ benchmark.

Joint training improves accuracy in data-scarce domains.

Expands geometric and flow diversity with ThingiCFD dataset.

Abstract

Accurate modeling of surface pressure fields around objects is fundamental to aerodynamic analysis and design. While neural networks have shown promise as efficient alternatives to expensive Computational Fluid Dynamics (CFD) simulations, their applicability is often constrained by data scarcity and poor generalization across different aerodynamic domains. To address these challenges, we propose UniField, a unified framework that enables joint training across multiple aerodynamic domains including automobiles, trains, aircraft. UniField employs a shared geometry encoder to extract domain-agnostic representations from surface point clouds, and integrates domain-specific flow information through Parallel Flow-Conditioned Adaptive LayerNorm (PFC-AdaLN). In addition to consolidating existing datasets from specialized research field including automobiles, trains and aircraft, we further introduce ThingiCFD, a large-scale CFD dataset constructed from Thingi10k geometries with extensive flow condition randomization, substantially expanding geometric and flow diversity during training. UniField achieves SOTA performance on the public DrivAerNet++ benchmark. In addition, our experiments demonstrate that joint multi-domain training consistently improves surface pressure prediction accuracy, particularly in data-scarce domains. These results highlight the potential of UniField as a foundation model for data-driven aerodynamic modeling. Code and data will be available at https://github.com/zoujunhong/UniField.