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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 51–100 of 347 papers

TitleStatusHype
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
Point-DeepONet: A Deep Operator Network Integrating PointNet for Nonlinear Analysis of Non-Parametric 3D Geometries and Load ConditionsCode1
KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics—0
A Library for Learning Neural OperatorsCode7
Conformal Prediction on Quantifying Uncertainty of Dynamic Systems—0
Operator Learning for Robust Stabilization of Linear Markov-Jumping Hyperbolic PDEs—0
A physics-informed transformer neural operator for learning generalized solutions of initial boundary value problemsCode1
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
Loss Terms and Operator Forms of Koopman AutoencodersCode0
Nonlinear Operator Learning Using Energy Minimization and MLPs—0
Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems—0
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
VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics PredictionCode1
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
PACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic DevicesCode1
The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures—0
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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