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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
Mondrian: Transformer Operators via Domain Decomposition—0
MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems—0
Towards scientific machine learning for granular material simulations -- challenges and opportunities—0
Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems—0
Multi-fidelity wavelet neural operator with application to uncertainty quantification—0
Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs—0
Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flows—0
Multi-Level Monte Carlo Training of Neural Operators—0
Multi-Physics Simulations via Coupled Fourier Neural Operator—0
Transfer Operator Learning with Fusion Frame—0
Multiscale Attention via Wavelet Neural Operators for Vision Transformers—0
Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers—0
Bayesian Inversion with Neural Operator (BINO) for Modeling Subdiffusion: Forward and Inverse Problems—0
Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives—0
Neural Basis Functions for Accelerating Solutions to High Mach Euler Equations—0
Accelerating Phase Field Simulations Through a Hybrid Adaptive Fourier Neural Operator with U-Net Backbone—0
Artificial intelligence for partial differential equations in computational mechanics: A review—0
Neural Inverse Operators for Solving PDE Inverse Problems—0
Improved generalization with deep neural operators for engineering systems: Path towards digital twin—0
Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations—0
Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks—0
Transformers as Neural Operators for Solutions of Differential Equations with Finite Regularity—0
A Resolution Independent Neural Operator—0
Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms—0
Accelerating Part-Scale Simulation in Liquid Metal Jet Additive Manufacturing via Operator Learning—0
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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