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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
Latent Neural Operator Pretraining for Solving Time-Dependent PDEs—0
ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines—0
Energy-Dissipative Evolutionary Deep Operator Neural Networks—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 Flame Evolution Operator under Hybrid Darrieus Landau and Diffusive Thermal Instability—0
Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)—0
Nonparametric Sparse Online Learning of the Koopman Operator—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
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
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
Image operator learning coupled with CNN classification and its application to staff line removal—0
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