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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 151–200 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
Operator Learning: Algorithms and Analysis—0
Smooth and Sparse Latent Dynamics in Operator Learning with Jerk Regularization—0
Diffeomorphism Neural Operator for various domains and parameters of partial differential equationsCode1
Invertible Fourier Neural Operators for Tackling Both Forward and Inverse ProblemsCode0
Parametric Learning of Time-Advancement Operators for Unstable Flame Evolution—0
Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs—0
Sobolev Training for Operator Learning—0
Learning time-dependent PDE via graph neural networks and deep operator network for robust accuracy on irregular grids—0
DIMON: Learning Solution Operators of Partial Differential Equations on a Diffeomorphic Family of Domains—0
An operator learning perspective on parameter-to-observable mapsCode1
Learning Operators with Stochastic Gradient Descent in General Hilbert Spaces—0
Functional SDE approximation inspired by a deep operator network architecture—0
Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction—0
Operator learning without the adjointCode0
Unsupervised Solution Operator Learning for Mean-Field Games via Sampling-Invariant Parametrizations—0
Generalization Error Guaranteed Auto-Encoder-Based Nonlinear Model Reduction for Operator Learning—0
Equivariant Graph Neural Operator for Modeling 3D DynamicsCode2
PDE Generalization of In-Context Operator Networks: A Study on 1D Scalar Nonlinear Conservation LawsCode2
Integration of physics-informed operator learning and finite element method for parametric learning of partial differential equations—0
Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flows—0
Operator learning for hyperbolic partial differential equations—0
HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork—0
A Mathematical Guide to Operator Learning—0
Operator-learning-inspired Modeling of Neural Ordinary Differential Equations—0
GIT-Net: Generalized Integral Transform for Operator LearningCode0
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