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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 101–125 of 347 papers

TitleStatusHype
DeepONet Augmented by Randomized Neural Networks for Efficient Operator Learning in PDEs—0
Learning Hamiltonian Density Using DeepONet—0
High-fidelity Multiphysics Modelling for Rapid Predictions Using Physics-informed Parallel Neural Operator—0
DeepSeek vs. ChatGPT: A Comparative Study for Scientific Computing and Scientific Machine Learning Tasks—0
Connecting the geometry and dynamics of many-body complex systems with message passing neural operators—0
TANTE: Time-Adaptive Operator Learning via Neural Taylor Expansion—0
Pseudo-Physics-Informed Neural Operators: Enhancing Operator Learning from Limited Data—0
Optimization for Neural Operators can Benefit from Width—0
Multi-Physics Simulations via Coupled Fourier Neural Operator—0
In-Context Operator Learning for Linear Propagator Models—0
MeshONet: A Generalizable and Efficient Operator Learning Method for Structured Mesh Generation—0
ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines—0
Physics-Informed Latent Neural Operator for Real-time Predictions of Complex Physical Systems—0
Orthogonal greedy algorithm for linear operator learning with shallow neural network—0
Operator Learning for Reconstructing Flow Fields from Sparse Measurements: an Energy Transformer Approach—0
Approximation Rates in Fréchet Metrics: Barron Spaces, Paley-Wiener Spaces, and Fourier Multipliers—0
KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics—0
Conformal Prediction on Quantifying Uncertainty of Dynamic Systems—0
Operator Learning for Robust Stabilization of Linear Markov-Jumping Hyperbolic PDEs—0
FLRONet: Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements—0
Adversarial Autoencoders in Operator LearningCode0
Some Best Practices in Operator LearningCode0
Nonlinear Operator Learning Using Energy Minimization and MLPs—0
Loss Terms and Operator Forms of Koopman AutoencodersCode0
Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems—0
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