Automotive Aerodynamics

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12 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

CIS-Transolver: A Cross-Layer Incremental-State Neural Operator for Fine-Scale Automotive Aerodynamic Flow Prediction
Oct 19, 2026Jianglong Liu, Zhicheng Zhu, Jia Hao +1Neural Operators Transformer Neural Operator
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
SHIFT-Truck: A High-Fidelity Aerodynamics Dataset and Benchmark for Pickup Trucks
Sep 29, 2026Riddhiman Raut, Yin Yu, Aashwin Anand Mishra +4Neural Surrogate Modeling Distribution Shift Robustness
Luminary, San Mateo, CA, USA · Department of Aeronautics and Astronautics, Stanford University, Stanford, CA, USA
HGPTrans: Hierarchical Graph-Pooling Transolver for Automotive Aerodynamic Drag Coefficient Prediction
Sep 24, 2026Bo Liu, Fengli Zhang, Qiuli Luo +2Automotive Aerodynamics Neural Surrogate Modeling
Institute of Auto Engineering, BYD Auto Industry Co., Ltd., Shenzhen, China
Robostreet Flow: A Lightweight, Ultra-Low-Drag Electric Tractor and Four-Truck Hybrid Convoy Architecture for Minimum-Cost Point-to-Point Freight
Jul 28, 2026Wei Wang, Yiru Veronika Wang, Sumukh Veeramalla +2Intelligent Transportation Systems Automotive Aerodynamics
Robostreet Inc.
Knowledge-Constrained Shape Optimization with a Mixture-of-Experts Neural Operator for High-Confidence Design
Jul 7, 2026Wenhao Fan, Yuanwei Bin, Jianghan Gu +4Neural Surrogate Modeling Automotive Aerodynamics
School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China · 2Eastern Institute of Technology, Ningbo, Ningbo 315200, Zhejiang, China · 3TenFong Technology Co., Ltd., Shenzhen 518000, Guangdong, China +1
Neural Network-Based Parametric Model Reduction for Predicting Turbulent Flow for Different Vehicle Geometries
Jun 23, 2026Kazuto Ando, Rahul Bale, Akiyoshi Kuroda +1Automotive Aerodynamics Reduced-Order Modeling
Operations and Computer Technologies Division, RIKEN Center for Computational Science 7-1-26 Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan · Complex Phenomena Unified Simulation Research Team, RIKEN Center for Computational Science, 7-1-26 Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan · Department of Computational Science, Graduate School of System Informatics, Kobe University, 1-1, Rokkodai-cho, Nada-ku, Kobe, 657-8501, Hyogo, Japan
A Geometry-Aware Triplane Field Network for Vehicle Aerodynamic Prediction
Jun 5, 2026Kangkang Qi, Huiyu Yang, Keqi Ding +5PDE Surrogate Modeling Computational Fluid Dynamics
Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen 518055, China · Shenzhen Key Laboratory of Complex Aerospace Flows, Southern University of Science and Technology, Shenzhen 518055, China · Shenzhen Tenfong Technology Co., Ltd., Shenzhen 518000, China +1
Adapting Automotive Aerodynamics Surrogates to New Vehicle Families via Transfer Learning
May 27, 2026Seunghwan Keum, Alok WareyAutomotive Aerodynamics PDE Surrogate Modeling
General Motors Research and Development
RETO: A Rotary-Enhanced Transformer Operator for High-Fidelity Prediction of Automotive Aerodynamics
Apr 30, 2026Bojun Zhang, Huiyu Yang, Yunpeng Wang +4Rotary Positional Embeddings Automotive Aerodynamics
Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, 518055, China · Shenzhen Key Laboratory of Complex Aerospace Flows, Southern University of Science and Technology,Shenzhen 518055, China · Ningbo Key Laboratory of Advanced Manufacturing Simulation, Eastern Institute of Technology, Ningbo, 315200, China +1
Faster by Design: Interactive Aerodynamics via Neural Surrogates Trained on Expert-Validated CFD
Apr 20, 2026Nicholas Thumiger, Andrea Bartezzaghi, Mattia Rigotti +5PDE Surrogate Modeling Graph Neural Operators
IBM Research · Dallara Automobili
A Blueprint for Self-Evolving Coding Agents in Vehicle Aerodynamic Drag Prediction
Mar 23, 2026Jinhui Ren, Huaiming Li, Yabin Liu +9Automotive Aerodynamics Surrogate Modeling
Famou Agent Team, Baidu AI Cloud · IAT AI Team
CarBench: A Comprehensive Benchmark for Neural Surrogates on High-Fidelity 3D Car Aerodynamics
Nov 25, 2025Mohamed Elrefaie, Dule Shu, Matt Klenk +1Automotive Aerodynamics Neural Surrogate Modeling
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA · Schwarzman College of Computing, Massachusetts Institute of Technology, Cambridge, MA, USA · Future Product Innovation, Toyota Research Institute, Los Altos, CA, USA

Oct 19, 2026J/K move · Enter open · S save

CIS-Transolver: A Cross-Layer Incremental-State Neural Operator for Fine-Scale Automotive Aerodynamic Flow Prediction

Jianglong Liu, Zhicheng Zhu, Jia Hao, Liangyue Jia

School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

Neural Operators Transformer Neural Operator Automotive Aerodynamics Computational Fluid Dynamics

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Neural operators offer an efficient data-driven approach to rapid prediction of complex automotive aerodynamic flows. However, spectral bias and other factors limit accurate reconstruction of fine-scale flow variations near complex geometries. Existing studies improve the representation and fitting of fine-scale information mainly through information extraction and training objectives. Further study is needed to determine how extracted local details can be used continually in subsequent operator computations and contribute effectively to the overall flow-field representation. We propose CIS-Transolver, a cross-layer incremental-state neural operator that extends fine-scale modeling from local information extraction to its use across layers. The method extracts fine-scale information through local multiscale analysis and encodes it as geometric moment states that are propagated and updated across layers. By combining information from previous layers with feedback to the value representations in Physics-Attention, the method allows local details to contribute continually to subsequent modeling of global physical correlations. We systematically evaluate CIS-Transolver on three public automotive aerodynamics benchmarks, ShapeNet-Car, AhmedML, and DrivAerNet++, covering surface pressure fields and volumetric pressure and velocity fields. CIS-Transolver achieves the lowest mean relative L2 error among the evaluated methods for all five prediction metrics across the three benchmarks. The relative error reductions average 8.72% across the five metrics compared with the best baseline for each metric, reaching 16.38% for ShapeNet-Car pressure prediction and 15.60% for AhmedML velocity prediction. These results provide a new approach to fine-scale flow prediction for complex geometries.