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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
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
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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