Fourier Neural Operator
PulseAugur coverage of Fourier Neural Operator — every cluster mentioning Fourier Neural Operator across labs, papers, and developer communities, ranked by signal.
8 day(s) with sentiment data
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New AI Model Enhances Prostate Cancer PET Image Synthesis from CT Scans
Researchers have developed a new method called Lesion-Aware Adaptive Fourier Neural Operator (LAFNO) to improve the synthesis of PSMA PET images from CT scans for prostate cancer patients. Traditional deep learning mode…
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New AI model accelerates 3D-IC thermal simulation with improved accuracy
Researchers have developed a novel framework called Self-Attention U-Net Fourier Neural Operator (SAU-FNO) to address the challenges of thermal simulation in 3D integrated circuits (ICs). This new method combines self-a…
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New SEAM framework ensures global consistency in scientific machine learning
Researchers have introduced SEAM (Scientific Explanation-Admissibility Machines), a new framework designed to ensure global consistency in scientific machine learning models. Unlike traditional methods that validate mod…
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New neural operator slashes financial model calibration time
Researchers have developed a novel neural operator designed to significantly speed up the calibration process for local-stochastic volatility (LSV) models in quantitative finance. This new method, implemented using Deep…
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COMPOL framework enhances neural operator accuracy for multiphysics simulations
Researchers have introduced COMPOL, a new framework designed to enhance the accuracy of neural operators in multiphysics simulations. This framework extends existing architectures by integrating recurrent and attention-…
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New Physics-Informed Neural Operator Accelerates EUV Lithography Simulations
Researchers have developed a Physics-Informed Neural Operator (PINO) to improve the simulation of electromagnetic scattering problems in extreme ultraviolet (EUV) lithography. This PINO model, which factorizes into late…
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New research quantifies theory-to-practice gap in neural networks and operators
Researchers have analyzed the sampling complexity for learning with ReLU neural networks and neural operators, deriving upper bounds on convergence rates based on the number of samples. This work establishes a unified t…
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New hierarchical physics-embedded learning approach reduces extrapolation errors by 70%
Researchers have developed a novel hierarchical physics-embedded learning approach that leverages partially known physical laws for spatiotemporal systems. This method encodes known physical structures and their governi…
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New AI model DiffARFNO enhances inkjet printing droplet prediction
Researchers have developed a new framework called DiffARFNO to improve the prediction of droplet evolution in inkjet printing. This two-stage model combines an autoregressive Fourier Neural Operator (Fourier-MIONet) for…
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New AI framework integrates weather and turbine data for wind power forecasting
Researchers have developed a new multimodal framework for short-term wind power forecasting that integrates SCADA data from wind turbines with numerical weather prediction (NWP) forecasts. This approach addresses the ch…
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New Fourier Neural Operator improves modeling of fluid convection
Researchers have developed an enhanced Fourier Neural Operator (FNO) designed to model two-dimensional Rayleigh-Bénard convection. This improved FNO predicts time increments rather than complete solutions, resulting in …
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AI model learns dynamics of nonlinear Schrödinger equation
Researchers have developed a geometry-conditioned Fourier neural operator (FNO) to model the cubic nonlinear Schrödinger (NLS) equation on two-dimensional flat tori. This operator learns from the real and imaginary part…
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Higher-Order FNO advances neural operators for nonlinear PDEs · 2 sources tracked
Researchers have introduced the Higher-Order Fourier Neural Operator (HO-FNO), an advancement on the Fourier Neural Operator (FNO) designed to better handle nonlinear partial differential equations (PDEs). HO-FNO incorp…
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New neural network architectures tackle complex scientific computing problems · 8 sources tracked
Researchers are developing novel neural network architectures to solve complex partial differential equations (PDEs) and model dynamical systems. These include structure-oriented randomized neural networks (SO-RaNN) for…
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New Dataset and Pipeline for AI Modeling of Turbulent Flows
Researchers have developed a validated dataset and pipeline for training neural operators to model turbulent 3D obstructed channel flows. The lattice Boltzmann solver used in the pipeline has been rigorously verified ag…
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Conformal prediction offers new uncertainty guarantees for physics simulations
Researchers have introduced a novel application of split conformal prediction to neural operator-based physics simulations, offering distribution-free prediction intervals with formal coverage guarantees. This method, a…
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New FNO method uses lattice points for improved efficiency
Researchers have developed a new approach to Fourier Neural Operators (FNOs) that improves their efficiency and accuracy. By replacing standard tensor product grids with rank-1 lattice points and using a hyperbolic cros…
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New conformal prediction framework enhances uncertainty quantification for neural operators
Researchers have developed a new conformal prediction framework to quantify uncertainty in neural operator learning, specifically for the 2D incompressible Navier-Stokes equations. This method uses a perturbation-based …
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Physics-guided deep learning enhances flood prediction accuracy
Researchers have developed a new physics-guided deep learning framework for advanced flood prediction. This hybrid model combines UNet and Fourier Neural Operator architectures, integrating multi-modal remote sensing da…
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Multi-agent system adapts thermal-hydraulic AI models
Researchers have developed a novel multi-agent governance framework designed to enable online adaptation of thermal-hydraulic surrogate models. This system uses distinct agents for monitoring, diagnosis, adaptation, saf…