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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 251–300 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
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
Multiscale Attention via Wavelet Neural Operators for Vision Transformers—0
Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers—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
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
Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms—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
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
Nonlinear Operator Learning Using Energy Minimization and MLPs—0
Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities—0
Nonparametric Control Koopman Operators—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
On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operators—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
Operator learning for hyperbolic partial differential equations—0
Operator Learning for Reconstructing Flow Fields from Sparse Measurements: an Energy Transformer Approach—0
Operator Learning for Robust Stabilization of Linear Markov-Jumping Hyperbolic PDEs—0
Operator-learning-inspired Modeling of Neural Ordinary Differential Equations—0
Operator Learning Meets Numerical Analysis: Improving Neural Networks through Iterative Methods—0
Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective—0
Operator learning regularization for macroscopic permeability prediction in dual-scale flow problem—0
QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator LearningCode0
Ensemble and Mixture-of-Experts DeepONets For Operator LearningCode0
Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular DynamicsCode0
Aero-Nef: Neural Fields for Rapid Aircraft Aerodynamics SimulationsCode0
Learning Where to Learn: Training Distribution Selection for Provable OOD PerformanceCode0
An Operator Learning Framework for Spatiotemporal Super-resolution of Scientific SimulationsCode0
LeMON: Learning to Learn Multi-Operator NetworksCode0
Leray-Schauder Mappings for Operator LearningCode0
Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström methodCode0
Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator LearningCode0
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