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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
Scattering with Neural Operators—0
Scientific Machine Learning Seismology—0
Separable Cosparse Analysis Operator Learning—0
SetONet: A Deep Set-based Operator Network for Solving PDEs with permutation invariant variable input sampling—0
Smooth and Sparse Latent Dynamics in Operator Learning with Jerk Regularization—0
Sobolev Training for Operator Learning—0
Solving High-Dimensional Inverse Problems with Auxiliary Uncertainty via Operator Learning with Limited Data—0
Solving Partial Differential Equations in Different Domains by Operator Learning method Based on Boundary Integral Equations—0
Solving PDE-constrained Control Problems Using Operator Learning—0
Solving Seismic Wave Equations on Variable Velocity Models with Fourier Neural Operator—0
Solving the Electrical Impedance Tomography Problem with a DeepONet Type Neural Network: Theory and Application—0
Spatio-spectral graph neural operator for solving computational mechanics problems on irregular domain and unstructured grid—0
Spectral operator learning for parametric PDEs without data reliance—0
Memory-efficient model-based deep learning with convergence and robustness guarantees—0
Stratospheric aerosol source inversion: Noise, variability, and uncertainty quantification—0
Super Resolution Based on Deep Operator Networks—0
The Parametric Complexity of Operator Learning—0
Nonlocality and Nonlinearity Implies Universality in Operator Learning—0
Theory-to-Practice Gap for Neural Networks and Neural Operators—0
Total Uncertainty Quantification in Inverse PDE Solutions Obtained with Reduced-Order Deep Learning Surrogate Models—0
Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics—0
Towards Scalable Koopman Operator Learning: Convergence Rates and A Distributed Learning Algorithm—0
Towards scientific machine learning for granular material simulations -- challenges and opportunities—0
Transfer Operator Learning with Fusion Frame—0
Transformers as Neural Operators for Solutions of Differential Equations with Finite Regularity—0
Two-level overlapping additive Schwarz preconditioner for training scientific machine learning applications—0
Uncertainty quantification for deeponets with ensemble kalman inversion—0
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression—0
Unsupervised Solution Operator Learning for Mean-Field Games via Sampling-Invariant Parametrizations—0
U-WNO:U-Net-enhanced Wavelet Neural Operator for fetal head segmentation—0
Variable-Input Deep Operator Networks—0
Variational Autoencoding Neural Operators—0
Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations—0
ViTO: Vision Transformer-Operator—0
Waveformer for modelling dynamical systems—0
Wavelet neural operator: a neural operator for parametric partial differential equations—0
Learning Operators with Stochastic Gradient Descent in General Hilbert Spaces—0
Learning the Hodgkin-Huxley Model with Operator Learning Techniques—0
Learning the Update Operator for 2D/3D Image Registration—0
Learning time-dependent PDE via graph neural networks and deep operator network for robust accuracy on irregular grids—0
Learning with SASQuaTCh: a Novel Variational Quantum Transformer Architecture with Kernel-Based Self-Attention—0
Local monotone operator learning using non-monotone operators: MnM-MOL—0
M2NO: Multiresolution Operator Learning with Multiwavelet-based Algebraic Multigrid Method—0
MD-NOMAD: Mixture density nonlinear manifold decoder for emulating stochastic differential equations and uncertainty propagation—0
MeshONet: A Generalizable and Efficient Operator Learning Method for Structured Mesh Generation—0
MgNO: Efficient Parameterization of Linear Operators via Multigrid—0
Minimax Optimal Kernel Operator Learning via Multilevel Training—0
Mitigating Stop-and-Go Traffic Congestion with Operator Learning—0
Mixture of Experts Soften the Curse of Dimensionality in Operator Learning—0
MODNO: Multi Operator Learning With Distributed Neural Operators—0
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