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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 76–100 of 347 papers

TitleStatusHype
Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?Code1
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
DeepOSets: Non-Autoregressive In-Context Learning of Supervised Learning Operators—0
Super Resolution Based on Deep Operator Networks—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
Basis-to-Basis Operator Learning Using Function EncodersCode1
Scientific Machine Learning Seismology—0
Generative AI for fast and accurate statistical computation of fluidsCode1
In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator LearningCode0
Time-Series Forecasting, Knowledge Distillation, and Refinement within a Multimodal PDE Foundation ModelCode0
Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics—0
PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid DynamicsCode1
FB-HyDON: Parameter-Efficient Physics-Informed Operator Learning of Complex PDEs via Hypernetwork and Finite Basis Domain Decomposition—0
Stratospheric aerosol source inversion: Noise, variability, and uncertainty quantification—0
Component Fourier Neural Operator for Singularly Perturbed Differential Equations—0
Operator Learning with Gaussian ProcessesCode1
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