Transformer Neural Operator

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20 papers5 papers in the last four weeks, up 25% on the four weeks before. 0.0% of all new papers.

BiLoGo: Reducing the smoothing of localised responses in Transformer-based neural operators for sheet forming
Nov 6, 2026Yingxue Zhao, Haoran Li, Haosu Zhou +2Neural Surrogate Modeling Neural Operators
Dyson School of Design Engineering, Imperial College London, London, UK · Prometheus, UK
Beyond Affine Reconstruction: Multi-Resolution Transformer DeepONets for the Radiative Transfer Equation
Oct 24, 2026Tanguy Azéma, Marc Bocquet, Fatiha Nmira +3PDE Surrogate Modeling Deep Operator Networks
CEREA, ENPC, EDF R&D, Institut Polytechnique de Paris, Île-de-France, France · EDF R&D, Chatou, France · Research Division, FM, 1165 Boston-Providence Turnpike, Norwood, MA 02062, USA +1
CoPiT: A conditional Position-induced Transformer for aerodynamic field prediction on unstructured meshes
Oct 23, 2026Junfeng Chen, Yongchen Fan, Zhonghua Qiao +1Transformer Neural Operator Neural Operators
Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China · Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China · Department of Mathematics and Shenzhen International Center for Mathematics, Southern University of Science and Technology, Shenzhen 518055, China
RheOPT: Few-Shot Non-Newtonian Flow Prediction via Newtonian Pretraining of RheOFormer
Oct 22, 2026Maedeh Saberi, Amir Barati Farimani, Safa JamaliNeural Operators Computational Fluid Dynamics
Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115, USA · Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
CIS-Transolver: A Cross-layer Incremental-State Neural Operator for Fine-Scale Automotive Aerodynamic Flow Prediction
Oct 19, 2026Jianglong Liu, Zhicheng Zhu, Jia Hao +2Neural 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.
Efficient Generative Transformer Operators For Million-Point PDEs
Dec 4, 2025Armand Kassaï Koupaï, Lise Le Boudec, Patrick GallinariPDE Surrogate Modeling Transformer Neural Operator
Sorbonne Université, CNRS, ISIR, 75005 Paris, France · Criteo AI Lab, Paris, France

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BiLoGo: Reducing the smoothing of localised responses in Transformer-based neural operators for sheet forming

Yingxue Zhao, Haoran Li, Haosu Zhou, Tobias Pfaff+1

Dyson School of Design Engineering, Imperial College London, London, UK · Prometheus, UK

Neural Surrogate Modeling Neural Operators Graph Neural Operators Transformer Neural Operator

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Trial-and-error FE simulation of sheet forming could be computationally expensive, neural-network-based learned simulators are used to accelerate design. These simulators should accurately capture localised responses, such as thinning around sharp radii or near ribs, which determine formability. Transformer-based neural operators propagate information efficiently across the mesh but tend to smooth such localised responses. This study develops BiLoGo, a Bipartite Local-Global learned simulator with an FE-guided design that connects local node-element message passing with a Transformer-based neural operator for long-range information exchange. BiLoGo has three modules: a bidirectional node-element GraphSAGE model that learns nodal-elemental coupling on a bipartite graph and predicts each quantity on its native FE entity without interpolation; a dual-stream Transformer-based neural operator that processes blank deformation and tool contact with separate token sets, preventing localised contact information from being averaged with the whole-blank state; and a fusion and element update module that combines global and local features via gated residual fusion and updates element features conditioned on element-node kinematics. Evaluated on a dome-shaped cold forming case and two corner-shaped hot stamping cases (one with ribs), BiLoGo achieves the lowest positional and threshold-based thinning errors on all three cases compared with single-level and hierarchical graph-based models and Transformer-based neural operators. Near the ribs, the smoothing index of predicted thinning increases from 0.41-0.56 for Transformer-based neural operators to 0.94 for BiLoGo-U. Compared with the hierarchical graph network CAtt-BiUGNN, BiLoGo-T reduces mean positional error by 15.5% on the Corner case and BiLoGo-U by 8.3% on the Corner+Rib case, with 66-69% less training memory.