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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 1–50 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
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
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
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
Improve Representation for Imbalanced Regression through Geometric ConstraintsCode1
Cauchy Random Features for Operator Learning in Sobolev SpaceCode0
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
RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domainsCode1
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
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