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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
Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems—0
Connecting the geometry and dynamics of many-body complex systems with message passing neural operators—0
Conformal Prediction on Quantifying Uncertainty of Dynamic Systems—0
In-Context Operator Learning for Linear Propagator Models—0
Insights into analysis operator learning: From patch-based sparse models to higher-order MRFs—0
Integration of physics-informed operator learning and finite element method for parametric learning of partial differential equations—0
OmniFluids: Unified Physics Pre-trained Modeling of Fluid Dynamics—0
Super Resolution Based on Deep Operator Networks—0
Inverted Gaussian Process Optimization for Nonparametric Koopman Operator Discovery—0
ViTO: Vision Transformer-Operator—0
Invertible Koopman neural operator for data-driven modeling of partial differential equations—0
Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution—0
Nonparametric Sparse Online Learning of the Koopman Operator—0
Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning—0
KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics—0
Known Operator Learning and Hybrid Machine Learning in Medical Imaging --- A Review of the Past, the Present, and the Future—0
The Parametric Complexity of Operator Learning—0
Nonlocality and Nonlinearity Implies Universality in Operator Learning—0
Composite Bayesian Optimization In Function Spaces Using NEON -- Neural Epistemic Operator Networks—0
Latent Neural Operator Pretraining for Solving Time-Dependent PDEs—0
Learning cardiac activation and repolarization times with operator learning—0
Component Fourier Neural Operator for Singularly Perturbed Differential Equations—0
Learning epidemic trajectories through Kernel Operator Learning: from modelling to optimal control—0
Learning Flame Evolution Operator under Hybrid Darrieus Landau and Diffusive Thermal Instability—0
Learning Hamiltonian Density Using DeepONet—0
The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures—0
TANTE: Time-Adaptive Operator Learning via Neural Taylor Expansion—0
Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction—0
Learning Operators with Stochastic Gradient Descent in General Hilbert Spaces—0
Bridging scales in multiscale bubble growth dynamics with correlated fluctuations using neural operator learning—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
Theory-to-Practice Gap for Neural Networks and Neural Operators—0
Learning with SASQuaTCh: a Novel Variational Quantum Transformer Architecture with Kernel-Based Self-Attention—0
Waveformer for modelling dynamical systems—0
Total Uncertainty Quantification in Inverse PDE Solutions Obtained with Reduced-Order Deep Learning Surrogate Models—0
Active operator learning with predictive uncertainty quantification for partial differential equations—0
Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics—0
Local monotone operator learning using non-monotone operators: MnM-MOL—0
Towards Scalable Koopman Operator Learning: Convergence Rates and A Distributed Learning Algorithm—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
Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers—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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