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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
Data-driven operator learning for energy-efficient building control—0
Data-efficient operator learning for solving high Mach number fluid flow problems—0
Deep Koopman Learning using Noisy Data—0
DeepOFormer: Deep Operator Learning with Domain-informed Features for Fatigue Life Prediction—0
DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning—0
DeepONet Augmented by Randomized Neural Networks for Efficient Operator Learning in PDEs—0
DeepONet for Solving Nonlinear Partial Differential Equations with Physics-Informed Training—0
Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles—0
Deep Operator Learning Lessens the Curse of Dimensionality for PDEs—0
DeepOSets: Non-Autoregressive In-Context Learning of Supervised Learning Operators—0
DeepSeek vs. ChatGPT: A Comparative Study for Scientific Computing and Scientific Machine Learning Tasks—0
Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis—0
Alpha-VI DeepONet: A prior-robust variational Bayesian approach for enhancing DeepONets with uncertainty quantification—0
Dilated convolution neural operator for multiscale partial differential equations—0
Dimension reduction for derivative-informed operator learning: An analysis of approximation errors—0
DimOL: Dimensional Awareness as A New 'Dimension' in Operator Learning—0
DIMON: Learning Solution Operators of Partial Differential Equations on a Diffeomorphic Family of Domains—0
Discretization Error of Fourier Neural Operators—0
Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification—0
Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)—0
Domain Adaptive Safety Filters via Deep Operator Learning—0
DPA-WNO: A gray box model for a class of stochastic mechanics problem—0
Dynamic Gaussian Graph Operator: Learning parametric partial differential equations in arbitrary discrete mechanics problems—0
Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators—0
ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines—0
Energy-Dissipative Evolutionary Deep Operator Neural Networks—0
Ensemble models outperform single model uncertainties and predictions for operator-learning of hypersonic flows—0
Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective—0
Operator learning regularization for macroscopic permeability prediction in dual-scale flow problem—0
Operator learning with PCA-Net: upper and lower complexity bounds—0
opPINN: Physics-Informed Neural Network with operator learning to approximate solutions to the Fokker-Planck-Landau equation—0
Optimal deep learning of holomorphic operators between Banach spaces—0
Optimization for Neural Operators can Benefit from Width—0
Orthogonal greedy algorithm for linear operator learning with shallow neural network—0
Parametric Learning of Time-Advancement Operators for Unstable Flame Evolution—0
Physics and geometry informed neural operator network with application to acoustic scattering—0
Physics-Informed Deep B-Spline Networks for Dynamical Systems—0
Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems—0
Koopman operator learning using invertible neural networks—0
Physics-Informed Latent Neural Operator for Real-time Predictions of Complex Physical Systems—0
Physics-informed Multiple-Input Operators for efficient dynamic response prediction of structures—0
Physics informed WNO—0
PMNO: A novel physics guided multi-step neural operator predictor for partial differential equations—0
Holistic Physics Solver: Learning PDEs in a Unified Spectral-Physical Space—0
Projection Methods for Operator Learning and Universal Approximation—0
Pseudo-Physics-Informed Neural Operators: Enhancing Operator Learning from Limited Data—0
Quantifying Training Difficulty and Accelerating Convergence in Neural Network-Based PDE Solvers—0
Randomized prior wavelet neural operator for uncertainty quantification—0
Recurrent Neural Operators: Stable Long-Term PDE Prediction—0
Reduced Order Modeling of a MOOSE-based Advanced Manufacturing Model with Operator Learning—0
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