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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 301–347 of 347 papers

TitleStatusHype
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
DeepSeek vs. ChatGPT: A Comparative Study for Scientific Computing and Scientific Machine Learning Tasks—0
DeepOSets: Non-Autoregressive In-Context Learning of Supervised Learning Operators—0
Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis—0
Solving Seismic Wave Equations on Variable Velocity Models with Fourier Neural Operator—0
Diffeomorphic Latent Neural Operators for Data-Efficient Learning of Solutions to Partial Differential Equations—0
Deep Operator Learning Lessens the Curse of Dimensionality for PDEs—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
Solving the Electrical Impedance Tomography Problem with a DeepONet Type Neural Network: Theory and Application—0
Dynamic Gaussian Graph Operator: Learning parametric partial differential equations in arbitrary discrete mechanics problems—0
Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse 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
Wavelet neural operator: a neural operator for parametric partial differential equations—0
Energy-Dissipative Evolutionary Deep Operator Neural Networks—0
Spatio-spectral graph neural operator for solving computational mechanics problems on irregular domain and unstructured grid—0
Ensemble models outperform single model uncertainties and predictions for operator-learning of hypersonic flows—0
Spectral operator learning for parametric PDEs without data reliance—0
Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles—0
DeepONet for Solving Nonlinear Partial Differential Equations with Physics-Informed Training—0
A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains—0
Controlling Statistical, Discretization, and Truncation Errors in Learning Fourier Linear Operators—0
Error-in-variables modelling for operator learning—0
Fast and Accurate Reduced-Order Modeling of a MOOSE-based Additive Manufacturing Model with Operator Learning—0
DeepONet Augmented by Randomized Neural Networks for Efficient Operator Learning in PDEs—0
Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning—0
DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning—0
FB-HyDON: Parameter-Efficient Physics-Informed Operator Learning of Complex PDEs via Hypernetwork and Finite Basis Domain Decomposition—0
DeepOFormer: Deep Operator Learning with Domain-informed Features for Fatigue Life Prediction—0
FLRONet: Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements—0
Forecasting subcritical cylinder wakes with Fourier Neural Operators—0
Fourier-RNNs for Modelling Noisy Physics Data—0
Fredholm Integral Equations Neural Operator (FIE-NO) for Data-Driven Boundary Value Problems—0
Functional SDE approximation inspired by a deep operator network architecture—0
FUSE: Fast Unified Simulation and Estimation for PDEs—0
Generalization Error Guaranteed Auto-Encoder-Based Nonlinear Model Reduction for Operator Learning—0
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