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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 151–175 of 347 papers

TitleStatusHype
Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric spaceCode0
BiLO: Bilevel Local Operator Learning for PDE inverse problemsCode0
Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEsCode0
MD-NOMAD: Mixture density nonlinear manifold decoder for emulating stochastic differential equations and uncertainty propagation—0
Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations—0
A Hybrid Kernel-Free Boundary Integral Method with Operator Learning for Solving Parametric Partial Differential Equations In Complex Domains—0
Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and ExtrapolationCode1
Learning epidemic trajectories through Kernel Operator Learning: from modelling to optimal control—0
Mixture of Experts Soften the Curse of Dimensionality in Operator Learning—0
MODNO: Multi Operator Learning With Distributed Neural Operators—0
Composite Bayesian Optimization In Function Spaces Using NEON -- Neural Epistemic Operator Networks—0
A finite operator learning technique for mapping the elastic properties of microstructures to their mechanical deformationsCode1
Nonlinear model reduction for operator learningCode0
Learning with SASQuaTCh: a Novel Variational Quantum Transformer Architecture with Kernel-Based Self-Attention—0
Bridging scales in multiscale bubble growth dynamics with correlated fluctuations using neural operator learning—0
Neural Parameter Regression for Explicit Representations of PDE Solution Operators—0
A Pretraining-Finetuning Computational Framework for Material Homogenization—0
Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problemsCode0
Uncertainty quantification for deeponets with ensemble kalman inversion—0
Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström methodCode0
Dynamic Gaussian Graph Operator: Learning parametric partial differential equations in arbitrary discrete mechanics problems—0
Deep Learning Computed Tomography based on the Defrise and Clack AlgorithmCode1
Neural Operators with Localized Integral and Differential KernelsCode4
A novel data generation scheme for surrogate modelling with deep operator networks—0
Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context LearningCode1
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