Transformer Neural Operator

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15 papers3 papers in the last four weeks, level with 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
Digital Self-Interference Cancellation for In-Band Full-Duplex Systems Based on Transformer Neural Operators
Oct 14, 2026Zhijie Su, Shiquan Zhang, Qiaolin HeTransformer Neural Operator Wireless Communications
Department of Mathematics, Sichuan University, Chengdu 610065, China
Neural Scaling Laws of Transformer Operator Network
Sep 27, 2026Haoran Yan, Zhongjie Shi, Yuanzhe Xi +2Neural Network Generalization Neural Network Approximation Theory
School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332, USA · Department of Mathematics, Emory University, Atlanta, GA 30322, USA · School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Does Transolver really need a Transformer?
Sep 26, 2026Shizheng Wen, Siddhartha MishraNeural Operators Transformer Neural Operator
Seminar for Applied Mathematics, ETH Zurich, Switzerland
A panoramic aerodynamic performance prediction method for turbomachinery cascades using transformer-enhanced neural operator
Sep 13, 2026Qineng Wang, Zhendong Guo, Liming Song +1Transformer Neural Operator PDE Surrogate Modeling
Institute of Turbomachinery, Xi’an Jiaotong University, Xi’an, 710049, China · ENN Science and Technology Development China Co., Ltd., Langfang, 065001, China · Hebei Key Laboratory of Compact Fusion, Langfang, 065001, China
Enhancing generalization in endwall film cooling prediction: Incorporating the superposition principle into transformer-based neural operators
Sep 10, 2026Qineng Wang, Liming Song, Tianyuan Liu +1Transformer Neural Operator Neural Network Generalization
Institute of Turbomachinery, Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, People’s Republic of China · ENN Science and Technology Development China Co., Ltd., Langfang, Hebei, 065001, China · Hebei Key Laboratory of Compact Fusion, Langfang, Hebei, 065001, China
VATO: A Vortex-Force-Aware Transformer Operator for Unsteady Separated Aerofoil Flows
Sep 1, 2026Xingxin Yang, Zhan Zhang, Yichen Li +1Transformer Neural Operator PDE Operator Learning
aDepartment of Engineering, King’s College London, London, WC2R 2LS, UK
MoNo: Multiscale Optimal Transport Neural Operator for Solving PDEs on General Geometries
Aug 10, 2026Zijiang Yang, Xiaomeng Wu, Dongmei FuNeural PDE Solvers PDE Operator Learning
School of Automation and Electrical Engineering, University of Science and Technology Beijing · 2Beijing Engineering Research Center of Industrial Spectrum Imaging
LLT: Local Linear Transformer for PDE Operator Learning
Jul 4, 2026Oded Ovadia, Eli TurkelPDE Surrogate Modeling Transformer Attention
School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel
Temperature Field Reconstruction of Tungsten Monoblock Divertor on EAST using Physics-aware Neural Operator Transformer
Jun 30, 2026Zikang Yan, Xiao Wang, Qingquan Yang +6Transformer Neural Operator PDE Surrogate Modeling
School of Computer Science and Technology, Anhui University, Hefei, China · Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, China · Tongling University, Tongling 244000, China +2
Zero-shot generalization of transformer neural operators to larger domains
Jun 12, 2026Armand de Villeroché, Sibo Cheng, Vincent Le Guen +5Transformer Neural Operator Local Attention
CEREA, ENPC, EDF R&D, Institut Polytechnique de Paris, Île-de-France, France · SINCLAIR AI Laboratory, Saclay, Île-de-France, France · EDF R&D, Île-de-France, France +1
Function graph transformers universally approximate operators between function spaces
May 18, 2026Takashi Furuya, David Mis, Ivan Dokmanić +2Transformer Neural Operator Transformer Attention
Doshisha University, RIKEN AIP · Rice University · University of Basel +2
Learning Neural Operator Surrogates for the Black Hole Accretion Code
Apr 28, 2026Matthias Nägele, Cedric Bös, Chester Tan +3Computational Fluid Dynamics Transformer Neural Operator
Institute for Theoretical Physics and Astrophysics, Julius-Maximilians-Universität Würzburg, Würzburg, Germany · Chair for Machine Learning for Complex Networks, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
Simple yet Effective: Low-Rank Spatial Attention for Neural Operators
Apr 4, 2026Zherui Yang, Haiyang Xin, Tao Du +1Neural Operators PDE Operator Learning
University of Science and Technology of China · Tsinghua University · Shanghai Qi Zhi Institute
Transolver-3: Scaling Up Transformer Solvers to Industrial-Scale Geometries
Feb 4, 2026Hang Zhou, Haixu Wu, Haonan Shangguan +4Efficient Neural Network Inference Neural PDE Solvers
School of Software, BNRist, Tsinghua University, China.

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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.