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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
Neural Operators for Predictor Feedback Control of Nonlinear Delay Systems—0
Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs—0
Two-level overlapping additive Schwarz preconditioner for training scientific machine learning applications—0
Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning—0
Neural Parameter Regression for Explicit Representations of PDE Solution Operators—0
Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study—0
New universal operator approximation theorem for encoder-decoder architectures (Preprint)—0
NOMAD: Nonlinear Manifold Decoders for Operator Learning—0
Uncertainty quantification for deeponets with ensemble kalman inversion—0
Nonlinear Operator Learning Using Energy Minimization and MLPs—0
Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities—0
Nonparametric Control Koopman Operators—0
Approximation Rates in Fréchet Metrics: Barron Spaces, Paley-Wiener Spaces, and Fourier Multipliers—0
On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators—0
One-shot learning for solution operators of partial differential equations—0
Online and Stable Learning of Analysis Operators—0
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression—0
On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operators—0
Accelerated primal-dual methods with enlarged step sizes and operator learning for nonsmooth optimal control problems—0
Unsupervised Solution Operator Learning for Mean-Field Games via Sampling-Invariant Parametrizations—0
Operator Learning: Algorithms and Analysis—0
Operator Learning: A Statistical Perspective—0
Operator Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations Characterized by Sharp Solutions—0
A Physics-Guided Bi-Fidelity Fourier-Featured Operator Learning Framework for Predicting Time Evolution of Drag and Lift Coefficients—0
Operator learning for hyperbolic partial differential equations—0
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