Operator learning

62 papers with code • 0 benchmarks • 1 datasets

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces


Use these libraries to find Operator learning models and implementations


Most implemented papers

Convolutional Analysis Operator Learning: Acceleration and Convergence

mechatoz/convolt 15 Feb 2018

This paper proposes a new convolutional analysis operator learning (CAOL) framework that learns an analysis sparsifying regularizer with the convolution perspective, and develops a new convergent Block Proximal Extrapolated Gradient method using a Majorizer (BPEG-M) to solve the corresponding block multi-nonconvex problems.

Physics-Informed Neural Operator for Learning Partial Differential Equations

devzhk/PINO 6 Nov 2021

Specifically, in PINO, we combine coarse-resolution training data with PDE constraints imposed at a higher resolution.

Convolutional Analysis Operator Learning: Dependence on Training Data

dahong67/ConvolutionalAnalysisOperatorLearning.jl 21 Feb 2019

Convolutional analysis operator learning (CAOL) enables the unsupervised training of (hierarchical) convolutional sparsifying operators or autoencoders from large datasets.

Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers

nvlabs/afno-transformer 24 Nov 2021

AFNO is based on a principled foundation of operator learning which allows us to frame token mixing as a continuous global convolution without any dependence on the input resolution.

GNOT: A General Neural Operator Transformer for Operator Learning

thu-ml/gnot 28 Feb 2023

However, there are several challenges for learning operators in practical applications like the irregular mesh, multiple input functions, and complexity of the PDEs' solution.

In-Context Operator Learning with Data Prompts for Differential Equation Problems

liuyangmage/in-context-operator-networks 17 Apr 2023

This paper introduces a new neural-network-based approach, namely In-Context Operator Networks (ICON), to simultaneously learn operators from the prompted data and apply it to new questions during the inference stage, without any weight update.

Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere

nvidia/torch-harmonics 6 Jun 2023

Fourier Neural Operators (FNOs) have proven to be an efficient and effective method for resolution-independent operator learning in a broad variety of application areas across scientific machine learning.

An enrichment approach for enhancing the expressivity of neural operators with applications to seismology

ehsanhaghighat/en-deeponet 7 Jun 2023

The Eikonal equation plays a central role in seismic wave propagation and hypocenter localization, a crucial aspect of efficient earthquake early warning systems.

Importance Weight Estimation and Generalization in Domain Adaptation under Label Shift

kazizzad/LabelShiftEstimator 29 Nov 2020

We deploy these estimators and provide generalization bounds in the unlabeled target domain.

Learning Symbolic Operators for Task and Motion Planning

ronuchit/LOFT_IROS_2021 28 Feb 2021

We then propose a bottom-up relational learning method for operator learning and show how the learned operators can be used for planning in a TAMP system.