LatticeGraphNet: a two-scale graph neural operator for simulating lattice structures
Sign Up to like & getrecommendations! Published in 2024 at "Engineering with Computers"
DOI: 10.1007/s00366-024-02034-7
Abstract: This study introduces a two-scale graph neural operator (GNO), namely, LatticeGraphNet (LGN), designed as a surrogate model for costly nonlinear finite-element simulations of three-dimensional latticed parts and structures. LGN has two networks: LGN-i, learning the… read more here.
Keywords: operator; graph neural; latticegraphnet two; two scale ... See more keywords
A boundary-based fourier neural operator (B-FNO) method for efficient parametric acoustic wave analysis
Sign Up to like & getrecommendations! Published in 2025 at "Engineering with Computers"
DOI: 10.1007/s00366-024-02103-x
Abstract: Repetitive wave analysis is required in various applications involving parametric analyses across different settings. However, traditional numerical methods based on domain discretization become computationally impractical due to the large number of simulations required, especially in… read more here.
Keywords: wave analysis; operator fno; analysis; fourier neural ... See more keywords
Accelerating phase field simulations through a hybrid adaptive Fourier neural operator with U-net backbone
Sign Up to like & getrecommendations! Published in 2024 at "npj Computational Materials"
DOI: 10.1038/s41524-024-01488-z
Abstract: Prolonged contact between a corrosive liquid and metal alloys can cause progressive dealloying. For one such process as liquid-metal dealloying (LMD), phase field models have been developed to understand the mechanisms leading to complex morphologies.… read more here.
Keywords: phase field; field; time; neural operator ... See more keywords
Fourier neural operator for large eddy simulation of compressible Rayleigh–Taylor turbulence
Sign Up to like & getrecommendations! Published in 2024 at "Physics of Fluids"
DOI: 10.1063/5.0213412
Abstract: The Fourier neural operator (FNO) framework is applied to the large eddy simulation (LES) of three-dimensional compressible Rayleigh–Taylor turbulence with miscible fluids at Atwood number At=0.5, stratification parameter Sr = 1.0, and Reynolds numbers Re = 10 000 and 30 000.… read more here.
Keywords: large eddy; turbulence; eddy simulation; fourier neural ... See more keywords
SSNO: Spatio-spectral Neural Operator for Functional Space Learning of Partial Differential Equations.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3148401
Abstract: Recent research to solve the parametric partial differential equations shifted the focus of conventional neural networks from finite-dimensional Euclidean space to generalized functional spaces. Neural operators learn the generalized function mapping directly, which was achieved… read more here.
Keywords: partial differential; spatio spectral; spectral neural; neural operator ... See more keywords
A Neural Operator Unifying Graph Neural Networks and Point Transformers
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Access"
DOI: 10.1109/access.2025.3585920
Abstract: Neural operators have emerged as a powerful tool for learning mappings between function spaces, particularly for solving partial differential equations (PDEs). This study introduces a novel framework that unifies Graph Neural Networks (GNNs) and Transformers,… read more here.
Keywords: unifying graph; operator; operator unifying; graph neural ... See more keywords
A Fourier Neural Operator Enhanced Physics-Embedded Iterative Learning Solver for Electromagnetic Scattering Analysis
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Antennas and Wireless Propagation Letters"
DOI: 10.1109/lawp.2025.3552500
Abstract: In this letter, an efficient and accurate physics-embedded Fletcher–Reeves conjugate gradient (FRCG) projection method, termed conjugate gradient-based Fourier neural operator (CGFNO), is proposed to solve the electromagnetic scattering problems iteratively. As a neuralnetwork, the Fourier… read more here.
Keywords: electromagnetic scattering; fourier neural; neural operator; physics embedded ... See more keywords
Spherical Neural Operator Network for Global Weather Prediction
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2023.3337857
Abstract: Global weather forecast is an important spatial-temporal prediction problem, which can provide numerous societal benefits such as extreme weather forewarning, traffic scheduling, and agricultural planning. Though many spatial-temporal prediction models have been proposed, they suffer… read more here.
Keywords: global weather; spherical neural; weather; prediction ... See more keywords
Rapid Surrogate Modeling of Electromagnetic Data in Frequency Domain Using Neural Operator
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3222507
Abstract: The efficiency of solving geophysical inverse problem largely relies on the efficiency of solving the corresponding forward problem. As for electromagnetic (EM) data forward modeling in frequency domain, the conventional numerical methods, e.g., finite difference… read more here.
Keywords: problem; frequency domain; operator; neural operator ... See more keywords
Advanced Spatial Temperature Monitoring of Power Modules via Fourier Neural Operator Based Thermal Model
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Power Electronics"
DOI: 10.1109/tpel.2025.3611030
Abstract: Accurate monitoring of power module spatial temperature (PMST) remains a critical challenge in power electronics. This letter proposes a Fourier neural operator-based thermal model (FNO-TM) to enable efficient and precise prediction of PMST for the… read more here.
Keywords: spatial temperature; power; operator based; fourier neural ... See more keywords
Memristive floating-point Fourier neural operator network for efficient scientific modeling
Sign Up to like & getrecommendations! Published in 2025 at "Science Advances"
DOI: 10.1126/sciadv.adv4446
Abstract: Emerging artificial intelligence for science (AI-for-Science) algorithms, such as the Fourier neural operator (FNO), enabled fast and efficient scientific simulation. However, extensive data transfers and intensive high-precision computing are necessary for network training, which challenges… read more here.
Keywords: efficient scientific; floating point; fourier neural; neural operator ... See more keywords