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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 276–300 of 347 papers

TitleStatusHype
On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators—0
Deep Operator Learning Lessens the Curse of Dimensionality for PDEs—0
Neural Inverse Operators for Solving PDE Inverse Problems—0
Forecasting subcritical cylinder wakes with Fourier Neural Operators—0
On the limits of neural network explainability via descramblingCode0
Improved generalization with deep neural operators for engineering systems: Path towards digital twin—0
Guiding continuous operator learning through Physics-based boundary constraintsCode1
An Introduction to Kernel and Operator Learning Methods for Homogenization by Self-consistent Clustering Analysis—0
Transform Once: Efficient Operator Learning in Frequency DomainCode1
Fast Sampling of Diffusion Models via Operator LearningCode1
Bayesian Inversion with Neural Operator (BINO) for Modeling Subdiffusion: Forward and Inverse Problems—0
Learning dynamical systems: an example from open quantum system dynamicsCode1
QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator LearningCode0
Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis—0
Mitigating spectral bias for the multiscale operator learning—0
A Kernel Approach for PDE Discovery and Operator Learning—0
Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities—0
Minimax Optimal Kernel Operator Learning via Multilevel Training—0
Solving Seismic Wave Equations on Variable Velocity Models with Fourier Neural Operator—0
Learning Efficient Abstract Planning Models that Choose What to PredictCode1
Multi-fidelity wavelet neural operator with application to uncertainty quantification—0
Neural Basis Functions for Accelerating Solutions to High Mach Euler Equations—0
Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers—0
opPINN: Physics-Informed Neural Network with operator learning to approximate solutions to the Fokker-Planck-Landau equation—0
Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations—0
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