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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 151–200 of 347 papers

TitleStatusHype
Stratospheric aerosol source inversion: Noise, variability, and uncertainty quantification—0
Component Fourier Neural Operator for Singularly Perturbed Differential Equations—0
State-space models are accurate and efficient neural operators for dynamical systemsCode0
Spatio-spectral graph neural operator for solving computational mechanics problems on irregular domain and unstructured grid—0
LeMON: Learning to Learn Multi-Operator NetworksCode0
An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning—0
Transfer Operator Learning with Fusion Frame—0
Total Uncertainty Quantification in Inverse PDE Solutions Obtained with Reduced-Order Deep Learning Surrogate Models—0
Fredholm Integral Equations Neural Operator (FIE-NO) for Data-Driven Boundary Value Problems—0
Controlling Statistical, Discretization, and Truncation Errors in Learning Fourier Linear Operators—0
Synergistic Learning with Multi-Task DeepONet for Efficient PDE Problem SolvingCode0
Alpha-VI DeepONet: A prior-robust variational Bayesian approach for enhancing DeepONets with uncertainty quantification—0
Aero-Nef: Neural Fields for Rapid Aircraft Aerodynamics SimulationsCode0
Solving the Electrical Impedance Tomography Problem with a DeepONet Type Neural Network: Theory and Application—0
HyperbolicLR: Epoch insensitive learning rate schedulerCode0
A Resolution Independent Neural Operator—0
Dilated convolution neural operator for multiscale partial differential equations—0
A fast neural hybrid Newton solver adapted to implicit methods for nonlinear dynamics—0
Green Multigrid Network—0
Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning—0
Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective—0
Accelerating Phase Field Simulations Through a Hybrid Adaptive Fourier Neural Operator with U-Net Backbone—0
Optimal deep learning of holomorphic operators between Banach spaces—0
Projection Methods for Operator Learning and Universal Approximation—0
Hamilton-Jacobi Based Policy-Iteration via Deep Operator Learning—0
Two-level overlapping additive Schwarz preconditioner for training scientific machine learning applications—0
M2NO: Multiresolution Operator Learning with Multiwavelet-based Algebraic Multigrid Method—0
GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applicationsCode0
Solving Partial Differential Equations in Different Domains by Operator Learning method Based on Boundary Integral Equations—0
Learning the Hodgkin-Huxley Model with Operator Learning Techniques—0
Physics and geometry informed neural operator network with application to acoustic scattering—0
Transformers as Neural Operators for Solutions of Differential Equations with Finite Regularity—0
Infinite-dimensional Diffusion Bridge Simulation via Operator LearningCode0
DPHGNN: A Dual Perspective Hypergraph Neural NetworksCode0
Deep Koopman Learning using Noisy Data—0
Data Complexity Estimates for Operator Learning—0
FUSE: Fast Unified Simulation and Estimation for PDEs—0
A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains—0
Hierarchical Neural Operator Transformer with Learnable Frequency-aware Loss Prior for Arbitrary-scale Super-resolution—0
Ensemble and Mixture-of-Experts DeepONets For Operator LearningCode0
Nonparametric Sparse Online Learning of the Koopman Operator—0
Nonparametric Control Koopman Operators—0
Learning Flame Evolution Operator under Hybrid Darrieus Landau and Diffusive Thermal Instability—0
Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric spaceCode0
Discretization Error of Fourier Neural Operators—0
BiLO: Bilevel Local Operator Learning for PDE inverse problemsCode0
Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEsCode0
Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations—0
MD-NOMAD: Mixture density nonlinear manifold decoder for emulating stochastic differential equations and uncertainty propagation—0
A Hybrid Kernel-Free Boundary Integral Method with Operator Learning for Solving Parametric Partial Differential Equations In Complex Domains—0
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