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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
Graph-Based Operator Learning from Limited Data on Irregular Domains—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
A Pretraining-Finetuning Computational Framework for Material Homogenization—0
Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)—0
Nonparametric Sparse Online Learning of the Koopman Operator—0
Controlling Statistical, Discretization, and Truncation Errors in Learning Fourier Linear Operators—0
Error-in-variables modelling for operator learning—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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