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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 201–250 of 347 papers

TitleStatusHype
Local monotone operator learning using non-monotone operators: MnM-MOL—0
DFU: scale-robust diffusion model for zero-shot super-resolution image generationCode0
Data-efficient operator learning for solving high Mach number fluid flow problems—0
Deciphering and integrating invariants for neural operator learning with various physical mechanismsCode1
Neural Dynamical Operator: Continuous Spatial-Temporal Model with Gradient-Based and Derivative-Free Optimization MethodsCode1
Equivariant Neural Operator Learning with Graphon ConvolutionCode1
A Physics-Guided Bi-Fidelity Fourier-Featured Operator Learning Framework for Predicting Time Evolution of Drag and Lift Coefficients—0
An Operator Learning Framework for Spatiotemporal Super-resolution of Scientific SimulationsCode0
Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator LearningCode0
Ensemble models outperform single model uncertainties and predictions for operator-learning of hypersonic flows—0
Operator Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations Characterized by Sharp Solutions—0
A foundational neural operator that continuously learns without forgetting—0
Adaptive operator learning for infinite-dimensional Bayesian inverse problems—0
Improved Operator Learning by Orthogonal AttentionCode1
MgNO: Efficient Parameterization of Linear Operators via Multigrid—0
Learning to Predict Structural VibrationsCode1
Waveformer for modelling dynamical systems—0
Spectral operator learning for parametric PDEs without data reliance—0
Operator Learning Meets Numerical Analysis: Improving Neural Networks through Iterative Methods—0
Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs—0
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
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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