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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 226–250 of 347 papers

TitleStatusHype
Fine-Tune Language Models as Multi-Modal Differential Equation SolversCode1
Fast and Accurate Reduced-Order Modeling of a MOOSE-based Additive Manufacturing Model with Operator Learning—0
Guaranteed Approximation Bounds for Mixed-Precision Neural OperatorsCode4
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
Operator Learning with Neural Fields: Tackling PDEs on General GeometriesCode1
Energy-Dissipative Evolutionary Deep Operator Neural Networks—0
An enrichment approach for enhancing the expressivity of neural operators with applications to seismologyCode1
Spherical Fourier Neural Operators: Learning Stable Dynamics on the SphereCode2
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
NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform DataCode1
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
Kernel Methods are Competitive for Operator LearningCode1
Nonlocality and Nonlinearity Implies Universality in Operator Learning—0
In-Context Operator Learning with Data Prompts for Differential Equation ProblemsCode1
Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems—0
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