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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
Learning epidemic trajectories through Kernel Operator Learning: from modelling to optimal control—0
Mixture of Experts Soften the Curse of Dimensionality in Operator Learning—0
MODNO: Multi Operator Learning With Distributed Neural Operators—0
Composite Bayesian Optimization In Function Spaces Using NEON -- Neural Epistemic Operator Networks—0
Nonlinear model reduction for operator learningCode0
Learning with SASQuaTCh: a Novel Variational Quantum Transformer Architecture with Kernel-Based Self-Attention—0
Bridging scales in multiscale bubble growth dynamics with correlated fluctuations using neural operator learning—0
Neural Parameter Regression for Explicit Representations of PDE Solution Operators—0
A Pretraining-Finetuning Computational Framework for Material Homogenization—0
Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problemsCode0
Uncertainty quantification for deeponets with ensemble kalman inversion—0
Dynamic Gaussian Graph Operator: Learning parametric partial differential equations in arbitrary discrete mechanics problems—0
Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström methodCode0
A novel data generation scheme for surrogate modelling with deep operator networks—0
Operator Learning: Algorithms and Analysis—0
Smooth and Sparse Latent Dynamics in Operator Learning with Jerk Regularization—0
Invertible Fourier Neural Operators for Tackling Both Forward and Inverse ProblemsCode0
Parametric Learning of Time-Advancement Operators for Unstable Flame Evolution—0
Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs—0
Sobolev Training for Operator Learning—0
Learning time-dependent PDE via graph neural networks and deep operator network for robust accuracy on irregular grids—0
DIMON: Learning Solution Operators of Partial Differential Equations on a Diffeomorphic Family of Domains—0
Learning Operators with Stochastic Gradient Descent in General Hilbert Spaces—0
Functional SDE approximation inspired by a deep operator network architecture—0
Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction—0
Operator learning without the adjointCode0
Unsupervised Solution Operator Learning for Mean-Field Games via Sampling-Invariant Parametrizations—0
Generalization Error Guaranteed Auto-Encoder-Based Nonlinear Model Reduction for Operator Learning—0
Integration of physics-informed operator learning and finite element method for parametric learning of partial differential equations—0
Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flows—0
Operator learning for hyperbolic partial differential equations—0
HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork—0
A Mathematical Guide to Operator Learning—0
Operator-learning-inspired Modeling of Neural Ordinary Differential Equations—0
GIT-Net: Generalized Integral Transform for Operator LearningCode0
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
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
MgNO: Efficient Parameterization of Linear Operators via Multigrid—0
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
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