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OneScience-Group/
Transolver-Car-Design

Name: Transolver-Car-Design
Rating: 2.5 (16 reviews)

OneScience

fluid dynamics

automotive aerodynamic design

CFD surrogate modeling

2402.02366

2502.02414

apache-2.0

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Transolver-Car-Design

Model Overview

Transolver-Car-Design is a three-dimensional automotive external-flow prediction model built on Transolver and Transolver++ by Tsinghua University's THUML group. It provides surrogate modeling of vehicle flow fields and rapid prediction of drag coefficients.

Paper: Transolver: A Fast Transformer Solver for PDEs on General Geometries

Model Description

Transolver-Car-Design uses a Transformer architecture with Physics-Attention and is trained on ShapeNet-Car automotive aerodynamic simulation data. It predicts velocity fields, pressure fields, and drag coefficients for complex vehicle geometries.

Use Cases

Use Case	Description
Automotive aerodynamic design	Rapidly predict external-flow velocities and surface pressures around vehicles
CFD surrogate modeling	Approximate fluid simulation on complex unstructured meshes with a neural network
Simulation acceleration	Provide a lightweight evaluation pipeline for large-scale candidate design screening

Usage

1. OneCode

Use the online OneCode environment for an intelligent, one-click AI for Science (AI4S) programming experience:

Launch OneCode for one-click AI4S programming

2. Manual Setup

Hardware Requirements

A GPU or DCU is recommended.
A CPU can be used for import checks and small-scale pipeline validation, but full training and inference will be slow.
DCU users must install DTK in advance. DTK 25.04.2 or later, or the OneScience-recommended version for the target cluster, is recommended.

Download the Model Package

bash

hf download OneScience-Group/Transolver-Car-Design --local-dir ./Transolver-Car-Design
cd Transolver-Car-Design

Set Up the Runtime Environment

DCU Environment

bash

# Activate DTK and Conda first
conda create -n onescience311 python=3.11 -y
conda activate onescience311
# Installation with uv is also supported
pip install onescience[cfd-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai

GPU Environment

bash

# Activate Conda first
conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12
conda activate onescience311
# Installation with uv is also supported
pip install onescience[cfd-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai

Training Data

The OneScience community provides the ShapeNetCar dataset for training. Download it with the command below and verify that the data path in config/config.yaml is configured correctly:

hf download --repo-type dataset OneScience-Group/ShapeNetCar --local-dir ./data

Training

python scripts/train.py

Training saves Transolver_plus.pth under weight/:

./weight/Transolver_plus.pth

Model Weights

This repository will provide model weights pretrained on ShapeNetCar data in the weights/ directory. The weights will be uploaded soon.

Inference

python scripts/inference.py

Evaluation and Visualization

python scripts/result.py

Official OneScience Resources

Platform	OneScience Repository	Skills Repository
Gitee	https://gitee.com/onescience-ai/onescience	https://gitee.com/onescience-ai/oneskills
GitHub	https://github.com/onescience-ai/OneScience	https://github.com/onescience-ai/oneskills

Citations and License

Original Transolver paper: Transolver: A Fast Transformer Solver for PDEs on General Geometries.
Original Transolver++ paper: Transolver++: An Accurate Neural Solver for PDEs on Million-Scale Geometries
This repository retains source attribution and has been adapted for automated execution through OneScience and ModelScope.