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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 201–225 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 problems—0
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
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