SOTAVerified

Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–275 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
Show:102550
← PrevPage 11 of 14Next →

No leaderboard results yet.