Graph Neural Operators

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10 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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

Oct 15, 2026J/K move · Enter open · S save

An Adaptive-Horizon Graph Neural Operator for coupled surrogate morphodynamic modelling

Ying Cao, Yifan Yang, Junqiang Xia, Julia C. Mullarney+1

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

Graph Neural Operators PDE Surrogate Modeling

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Morphodynamic simulations require repeated coupling between hydrodynamic solvers, sediment transport modules, and bed updating. For long duration, multi-scenario simulations on large unstructured meshes, such coupling is computationally demanding. This study proposes an Adaptive-Horizon Graph Neural Operator (AH-GNO) that formulates bed evolution as a graph operator learning problem from recent hydrodynamic and topographic states to predict future bed elevation increments. The model operates directly on the native unstructured meshes. The approach integrates area-weighted graph learning, historical state selection, and forecast horizon estimation, allowing the effective history length (K) and update interval (B) to vary with the evolving flow and bed state. Using Telemac2D/GAIA solutions as supervised numerical references, the framework is evaluated on the official Bump and Yen bend benchmarks and on a field-scale application (Waiho River, New Zealand). Results show that the AH-GNO reproduces the main patterns of bedform migration, bend-scale erosion and deposition, and localized bed adjustment. Across the three cases, the tested CPU-GPU deployment reduced post-training bed-update workflow time from 12~~1204 s with GAIA to 0.08~~3.34 s with AH-GNO. This improvement corresponds to deployment-specific accelerations of approximately 150~~360 times relative to 4-core GAIA CPU configurations, and with 105~~186 times gains for 20-core CPU configurations. These results suggest that, while retaining hydrodynamic feedback from TELEMAC-2D, a learned operator for bed updating can provide an efficient acceleration component for morphodynamic scenario analysis and rapid coupled simulation.