Fourier Neural Operator
PulseAugur coverage of Fourier Neural Operator — every cluster mentioning Fourier Neural Operator across labs, papers, and developer communities, ranked by signal.
6 day(s) with sentiment data
-
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…
-
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…
-
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…
-
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…
-
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 …
-
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…
-
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…
-
AI framework enhances SMR simulations for digital twins
Researchers have developed a novel framework combining reduced-order models (ROMs) with neural operators for computational fluid dynamics (CFD) simulations. This approach aims to enable real-time thermal-hydraulic simul…
-
New AI methods tackle complex inverse problems with improved sampling
Researchers are developing new methods to tackle complex inverse problems in machine learning, particularly in scenarios where gradient information is unavailable. New techniques aim to improve sampling from high-dimens…
-
AI Models Tackle Sparse Sensor Data for Physical Field Reconstruction
Researchers have developed several novel AI frameworks for reconstructing complex physical fields from limited sensor data. LASER utilizes a reinforcement learning policy within a latent world model to actively guide se…
-
New AI methods tackle complex differential equations
Researchers are exploring novel neural network architectures and training methodologies to enhance the solution of complex differential equations. Papers introduce reformulated neural operators that incorporate an auxil…
-
New neural operator integrates physics symmetries for improved generalization
Researchers have developed a new neural operator called PACE-FNO that better handles out-of-distribution scenarios by incorporating known continuous symmetries of evolution equations. This model separates the tasks of e…
-
New theory offers generalization bounds for PDE operator learning
Researchers have developed a theoretical framework for operator learning applied to nonlinear parabolic partial differential equations (PDEs). This approach focuses on learning solution operators from finite data, empha…
-
DuFal AI model enhances sparse-view CT scans by learning dual frequencies
Researchers have developed DuFal, a novel framework for reconstructing high-fidelity Computed Tomography (CT) volumes from extremely limited X-ray projections. This dual-path architecture integrates frequency-domain and…