Graph Neural Operators

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12 papers2 papers in the last four weeks, against 2 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
MIGONet: a multiple-input graph neural operator with solver-free discrete manufactured-solution training
Nov 4, 2026Muhammad Hasnain, N. K. Anand, Arun SrinivasaPDE Surrogate Modeling Neural Operators
Department of Mechanical Engineering, Texas A&M University, 3123 TAMU College Station, TX 77843-3123, USA
An Adaptive-Horizon Graph Neural Operator for coupled surrogate morphodynamic modelling
Oct 15, 2026Ying Cao, Yifan Yang, Junqiang Xia +2Graph Neural Operators PDE Surrogate Modeling
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China · Coastal Marine Group, School of Science, The University of Waikato, Hamilton, New Zealand · Institute for Water and Environment (IWU), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
A physics-coupled graph neural operator for mid-plane six-component stress tensor field prediction in concrete penetration
Oct 12, 2026Wenpu Du, Peng Zhou, Yunlong Xia +5Graph Neural Operators Neural Operators
School of Environment and Safety Engineering, North University of China, Taiyuan, 030051, China
FTD-GNO: Memory-Efficient Graph Neural Operators through Functional Tensor Decomposition of the Kernel
Oct 3, 2026Xiaomin Zhang, Boyue Wang, Junbin Gao +1Graph Neural Operators Neural Operators
Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Artificial Intelligence Institute School of Information Science and Technology Beijing University of Technology, Beijing 100124, China · Discipline of Business Analytics The University of Sydney Business School The University of Sydney, Camperdown NSW 2006, Australia
Field-level prediction of mid-plane stress tensor fields in concrete target penetration: a cross-velocity graph neural operator surrogate
Sep 9, 2026Wenpu Du, Peng Zhou, Yunlong Xia +5Neural Surrogate Modeling Benchmark Construction
School of Environment and Safety Engineering, North University of China, Taiyuan, 030051, China
GRIFDIR: Graph Resolution-Invariant FEM Diffusion Models in Function Spaces over Irregular Domains
May 5, 2026James Rowbottom, Elizabeth L. Baker, Nick Huang +3Graph Neural Operators Finite Element Method
Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK · Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark · Department of Mathematics, Simon Fraser University, Canada +1
An ALE-Consistent Graph Neural Operator-Transformer Framework for Fluid-Structure Interaction
May 1, 2026Shihang Zhao, Martín Saravia, Haokui Jiang +2PDE Surrogate Modeling Graph Neural Operators
Institute for Ocean Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, 518055, China · Centro de Investigación en Mecánica Teórica y Aplicada, Universidad Tecnológica Nacional - CONICET, 11 de Abril 461, Bahía Blanca, Argentina
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
Neuroscience Inspired Graph Operators Towards Edge-Deployable Virtual Sensing for Irregular Geometries
Apr 17, 2026William Howes, Farid Ahmed, Kazuma Kobayashi +2Edge Computing Graph Neural Operators
Grainger College of Engineering, Nuclear, Plasma & Radiological Engineering Department, University of Illinois Urbana-Champaign, Urbana, IL, USA · Department of Applied Mechanics, Indian Institute of Technology Delhi, New Delhi, India · Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi +1
Real-Time Sensing of Inaccessible Physical Fields via an Edge-Deployable Hardware-Portable Graph Neural Operator
Apr 2, 2026William Howes, Jason Yoo, Kazuma Kobayashi +4Graph Neural Operators Neural Operators
Grainger College of Engineering, Nuclear, Plasma & Radiological Engineering Department, University of Illinois Urbana-Champaign, Urbana, IL, USA · Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi · Department of Applied Mechanics, Indian Institute of Technology Delhi, New Delhi, India +1
GIST: Gauge-Invariant Spectral Transformers for Scalable Graph Neural Operators
Mar 17, 2026Mattia Rigotti, Nicholas Thumiger, Thomas FrickGraph Neural Operators Neural Operators
IBM Research

Nov 6, 2026J/K move · Enter open · S save

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.