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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 126–150 of 347 papers

TitleStatusHype
Operator learning regularization for macroscopic permeability prediction in dual-scale flow problem—0
Neural Operators for Predictor Feedback Control of Nonlinear Delay Systems—0
Diffeomorphic Latent Neural Operators for Data-Efficient Learning of Solutions to Partial Differential Equations—0
U-WNO:U-Net-enhanced Wavelet Neural Operator for fetal head segmentation—0
DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning—0
Mitigating Stop-and-Go Traffic Congestion with Operator Learning—0
The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures—0
Latent Neural Operator Pretraining for Solving Time-Dependent PDEs—0
On the Benefits of Active Data Collection in Operator LearningCode0
Artificial intelligence for partial differential equations in computational mechanics: A review—0
Domain Adaptive Safety Filters via Deep Operator Learning—0
Holistic Physics Solver: Learning PDEs in a Unified Spectral-Physical Space—0
Super Resolution Based on Deep Operator Networks—0
DeepOSets: Non-Autoregressive In-Context Learning of Supervised Learning Operators—0
DimOL: Dimensional Awareness as A New 'Dimension' in Operator Learning—0
Quantifying Training Difficulty and Accelerating Convergence in Neural Network-Based PDE Solvers—0
DeepONet for Solving Nonlinear Partial Differential Equations with Physics-Informed Training—0
Leray-Schauder Mappings for Operator LearningCode0
Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study—0
Structure-Preserving Operator LearningCode0
Scientific Machine Learning Seismology—0
In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator LearningCode0
Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics—0
Time-Series Forecasting, Knowledge Distillation, and Refinement within a Multimodal PDE Foundation ModelCode0
FB-HyDON: Parameter-Efficient Physics-Informed Operator Learning of Complex PDEs via Hypernetwork and Finite Basis Domain Decomposition—0
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