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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 1–25 of 347 papers

TitleStatusHype
Mesh-Informed Neural Operator : A Transformer Generative ApproachCode1
OmniFluids: Unified Physics Pre-trained Modeling of Fluid Dynamics—0
Principled Approaches for Extending Neural Architectures to Function Spaces for Operator LearningCode1
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
Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary DomainsCode2
Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical SystemsCode0
Operator Learning for Schrödinger Equation: Unitarity, Error Bounds, and Time GeneralizationCode0
Multi-Level Monte Carlo Training of Neural Operators—0
Neural Functional: Learning Function to Scalar Maps for Neural PDE SurrogatesCode0
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
DeepOHeat-v1: Efficient Operator Learning for Fast and Trustworthy Thermal Simulation and Optimization in 3D-IC DesignCode0
Operator Learning: A Statistical Perspective—0
A Physics-Informed Meta-Learning Framework for the Continuous Solution of Parametric PDEs on Arbitrary GeometriesCode1
Towards scientific machine learning for granular material simulations -- challenges and opportunities—0
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