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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 101–150 of 347 papers

TitleStatusHype
Bridging scales in multiscale bubble growth dynamics with correlated fluctuations using neural operator learning—0
ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines—0
KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics—0
TANTE: Time-Adaptive Operator Learning via Neural Taylor Expansion—0
Ensemble models outperform single model uncertainties and predictions for operator-learning of hypersonic flows—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 Operators with Stochastic Gradient Descent in General Hilbert Spaces—0
Nonparametric Sparse Online Learning of the Koopman Operator—0
Controlling Statistical, Discretization, and Truncation Errors in Learning Fourier Linear Operators—0
Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)—0
Fast and Accurate Reduced-Order Modeling of a MOOSE-based Additive Manufacturing Model with Operator Learning—0
Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification—0
Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning—0
Discretization Error of Fourier Neural Operators—0
DIMON: Learning Solution Operators of Partial Differential Equations on a Diffeomorphic Family of Domains—0
Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL)—0
FLRONet: Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements—0
DimOL: Dimensional Awareness as A New 'Dimension' in Operator Learning—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
Dimension reduction for derivative-informed operator learning: An analysis of approximation errors—0
Generalization Error Guaranteed Auto-Encoder-Based Nonlinear Model Reduction for Operator Learning—0
Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers—0
Dilated convolution neural operator for multiscale partial differential equations—0
Bayesian Inversion with Neural Operator (BINO) for Modeling Subdiffusion: Forward and Inverse Problems—0
Adaptive operator learning for infinite-dimensional Bayesian inverse problems—0
Alpha-VI DeepONet: A prior-robust variational Bayesian approach for enhancing DeepONets with uncertainty quantification—0
Diffeomorphic Latent Neural Operators for Data-Efficient Learning of Solutions to Partial Differential Equations—0
A Kernel Approach for PDE Discovery and Operator Learning—0
Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis—0
Artificial intelligence for partial differential equations in computational mechanics: A review—0
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation—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
A Resolution Independent Neural Operator—0
Deep Operator Learning Lessens the Curse of Dimensionality for PDEs—0
Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles—0
Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning—0
Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution—0
DeepONet for Solving Nonlinear Partial Differential Equations with Physics-Informed Training—0
DeepONet Augmented by Randomized Neural Networks for Efficient Operator Learning in PDEs—0
Approximation Rates in Fréchet Metrics: Barron Spaces, Paley-Wiener Spaces, and Fourier Multipliers—0
DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning—0
A Hybrid Kernel-Free Boundary Integral Method with Operator Learning for Solving Parametric Partial Differential Equations In Complex Domains—0
Accelerating Phase Field Simulations Through a Hybrid Adaptive Fourier Neural Operator with U-Net Backbone—0
Improved Model based Deep Learning using Monotone Operator Learning (MOL)—0
DeepOFormer: Deep Operator Learning with Domain-informed Features for Fatigue Life Prediction—0
Insights into analysis operator learning: From patch-based sparse models to higher-order MRFs—0
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