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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 101–125 of 347 papers

TitleStatusHype
Hamilton-Jacobi Based Policy-Iteration via Deep Operator Learning—0
ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines—0
Energy-Dissipative Evolutionary Deep Operator Neural Networks—0
TANTE: Time-Adaptive Operator Learning via Neural Taylor Expansion—0
Ensemble models outperform single model uncertainties and predictions for operator-learning of hypersonic flows—0
Component Fourier Neural Operator for Singularly Perturbed Differential Equations—0
Mitigating spectral bias for the multiscale operator learning—0
Improved Model based Deep Learning using Monotone Operator Learning (MOL)—0
Nonparametric Sparse Online Learning of the Koopman Operator—0
Controlling Statistical, Discretization, and Truncation Errors in Learning Fourier Linear Operators—0
Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)—0
Fast and Accurate Reduced-Order Modeling of a MOOSE-based Additive Manufacturing Model with Operator Learning—0
Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification—0
Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning—0
Discretization Error of Fourier Neural Operators—0
DIMON: Learning Solution Operators of Partial Differential Equations on a Diffeomorphic Family of Domains—0
Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL)—0
FLRONet: Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements—0
Forecasting subcritical cylinder wakes with Fourier Neural Operators—0
Fourier-RNNs for Modelling Noisy Physics Data—0
Fredholm Integral Equations Neural Operator (FIE-NO) for Data-Driven Boundary Value Problems—0
Functional SDE approximation inspired by a deep operator network architecture—0
DimOL: Dimensional Awareness as A New 'Dimension' in Operator Learning—0
Generalization Error Guaranteed Auto-Encoder-Based Nonlinear Model Reduction for Operator Learning—0
Dimension reduction for derivative-informed operator learning: An analysis of approximation errors—0
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