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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
Sharp Spectral Rates for Koopman Operator LearningCode1
DCEM: A deep complementary energy method for solid mechanicsCode1
Guiding continuous operator learning through Physics-based boundary constraintsCode1
Transform Once: Efficient Operator Learning in Frequency DomainCode1
Fast Sampling of Diffusion Models via Operator LearningCode1
Learning dynamical systems: an example from open quantum system dynamicsCode1
Learning Efficient Abstract Planning Models that Choose What to PredictCode1
Transformer for Partial Differential Equations' Operator LearningCode1
U-NO: U-shaped Neural OperatorsCode1
Deep transfer operator learning for partial differential equations under conditional shiftCode1
Learning Operators with Coupled AttentionCode1
Adaptive Fourier Neural Operators: Efficient Token Mixers for TransformersCode1
Physics-Informed Neural Operator for Learning Partial Differential EquationsCode1
Fast PDE-constrained optimization via self-supervised operator learningCode1
Improved architectures and training algorithms for deep operator networksCode1
Multiwavelet-based Operator Learning for Differential EquationsCode1
Choose a Transformer: Fourier or GalerkinCode1
Learning Symbolic Operators for Task and Motion PlanningCode1
OmniFluids: Unified Physics Pre-trained Modeling of Fluid Dynamics—0
Mondrian: Transformer Operators via Domain Decomposition—0
PMNO: A novel physics guided multi-step neural operator predictor for partial differential equations—0
Recurrent Neural Operators: Stable Long-Term PDE Prediction—0
Learning Where to Learn: Training Distribution Selection for Provable OOD PerformanceCode0
Graph-Based Operator Learning from Limited Data on Irregular Domains—0
Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical SystemsCode0
Operator Learning for Schrödinger Equation: Unitarity, Error Bounds, and Time GeneralizationCode0
Neural Functional: Learning Function to Scalar Maps for Neural PDE SurrogatesCode0
Multi-Level Monte Carlo Training of Neural Operators—0
Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers—0
Learning cardiac activation and repolarization times with operator learning—0
Physics-informed Multiple-Input Operators for efficient dynamic response prediction of structures—0
SetONet: A Deep Set-based Operator Network for Solving PDEs with permutation invariant variable input sampling—0
Data-driven operator learning for energy-efficient building control—0
A Hybrid Framework for Efficient Koopman Operator Learning—0
Geometry aware inference of steady state PDEs using Equivariant Neural Fields representationsCode0
Dimension reduction for derivative-informed operator learning: An analysis of approximation errors—0
Operator Learning: A Statistical Perspective—0
DeepOHeat-v1: Efficient Operator Learning for Fast and Trustworthy Thermal Simulation and Optimization in 3D-IC DesignCode0
Towards scientific machine learning for granular material simulations -- challenges and opportunities—0
Inverted Gaussian Process Optimization for Nonparametric Koopman Operator Discovery—0
New universal operator approximation theorem for encoder-decoder architectures (Preprint)—0
DeepOFormer: Deep Operator Learning with Domain-informed Features for Fatigue Life Prediction—0
EquiNO: A Physics-Informed Neural Operator for Multiscale SimulationsCode0
Invertible Koopman neural operator for data-driven modeling of partial differential equations—0
Data-Driven, ML-assisted Approaches to Problem Well-Posedness—0
Theory-to-Practice Gap for Neural Networks and Neural Operators—0
Physics-Informed Deep B-Spline Networks for Dynamical Systems—0
ON-Traffic: An Operator Learning Framework for Online Traffic Flow Estimation and Uncertainty Quantification from Lagrangian SensorsCode0
Active operator learning with predictive uncertainty quantification for partial differential equations—0
Cauchy Random Features for Operator Learning in Sobolev SpaceCode0
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