Fourier Neural Operator
PulseAugur coverage of Fourier Neural Operator — every cluster mentioning Fourier Neural Operator across labs, papers, and developer communities, ranked by signal.
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New neural operator VIOT accelerates incompressible flow transport
Researchers have developed a new generative neural operator called Variational Incompressible Optimal Transport (VIOT) designed for efficient density transport in incompressible flows. VIOT utilizes a stream-function or…
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New PI-CP method enhances uncertainty quantification for neural operators
Researchers have developed a new method called Physics-Informed Conformal Prediction (PI-CP) to provide reliable uncertainty estimates for neural operators used in approximating solutions to partial differential equatio…
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Neural Cellular Automata learn long-term PDE dynamics, outperforming baselines
Researchers have developed a novel Neural Cellular Automata (NCA) model designed to learn and predict the long-term dynamics of partial differential equations (PDEs). This NCA-based surrogate model operates by learning …
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AI models for physics: Caltech professor pioneers structure-driven approach
Anima Anandkumar, a professor at the California Institute of Technology, has pioneered the development of AI models for complex physical systems, challenging the prevailing notion that scale is the only path to progress…
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Neural operators learn Kohn-Sham map for faster DFT calculations
Researchers have developed a novel approach to density functional theory (DFT) by using neural operators to learn the Kohn-Sham map, which bypasses the computationally intensive orbital diagonalization step. This method…
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CarBench benchmark launched for 3D car aerodynamics AI models
Researchers have introduced CarBench, the first comprehensive benchmark for evaluating neural surrogate models in high-fidelity 3D car aerodynamics. This benchmark utilizes the DrivAerNet++ dataset, which comprises over…
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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…