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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 251–300 of 347 papers

TitleStatusHype
Multi-Resolution Active Learning of Fourier Neural OperatorsCode0
DPA-WNO: A gray box model for a class of stochastic mechanics problem—0
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation—0
Scattering with Neural Operators—0
Reduced Order Modeling of a MOOSE-based Advanced Manufacturing Model with Operator Learning—0
Fast and Accurate Reduced-Order Modeling of a MOOSE-based Additive Manufacturing Model with Operator Learning—0
Neural Operators for PDE Backstepping Control of First-Order Hyperbolic PIDE with Recycle and DelayCode0
Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)—0
Interpreting and generalizing deep learning in physics-based problems with functional linear modelsCode0
Accelerated primal-dual methods with enlarged step sizes and operator learning for nonsmooth optimal control problems—0
Koopman operator learning using invertible neural networks—0
The Parametric Complexity of Operator Learning—0
Energy-Dissipative Evolutionary Deep Operator Neural Networks—0
Globally injective and bijective neural operators—0
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression—0
Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles—0
Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning—0
Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular DynamicsCode0
Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives—0
Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution—0
Nonlocality and Nonlinearity Implies Universality in Operator Learning—0
Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems—0
Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equationsCode0
Neural Operator Learning for Ultrasound Tomography InversionCode0
Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL)—0
Operator learning with PCA-Net: upper and lower complexity bounds—0
Multiscale Attention via Wavelet Neural Operators for Vision Transformers—0
Solving High-Dimensional Inverse Problems with Auxiliary Uncertainty via Operator Learning with Limited Data—0
LNO: Laplace Neural Operator for Solving Differential EquationsCode0
Neural Operators of Backstepping Controller and Observer Gain Functions for Reaction-Diffusion PDEsCode0
ViTO: Vision Transformer-Operator—0
Super-Resolution Neural OperatorCode0
Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential EquationsCode0
Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks—0
Variational Autoencoding Neural Operators—0
Fourier-RNNs for Modelling Noisy Physics Data—0
Physics informed WNO—0
Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equationsCode0
IB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning—0
Randomized prior wavelet neural operator for uncertainty quantification—0
An Enhanced V-cycle MgNet Model for Operator Learning in Numerical Partial Differential Equations—0
Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning—0
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
An Introduction to Kernel and Operator Learning Methods for Homogenization by Self-consistent Clustering Analysis—0
Bayesian Inversion with Neural Operator (BINO) for Modeling Subdiffusion: Forward and Inverse Problems—0
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