Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

Affiliated Institutions

Related Publications

Network In Network

Min Lin , Qiang Chen , Shuicheng Yan

Abstract: We propose a novel deep network structure called In Network (NIN) to enhance model discriminability for local patches within the receptive field. The conventional con...

2014 arXiv (Cornell University) 1037 citations

Representation Learning: A Review and New Perspectives

Yoshua Bengio , Aaron Courville , P. M. Durai Raj Vincent

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more...

2013 IEEE Transactions on Pattern Analysis... 12373 citations

Fast Image Super-Resolution Based on In-Place Example Regression

Shuicheng Yan , Zhe Lin , Scott Cohen

We propose a fast regression model for practical single image super-resolution based on in-place examples, by leveraging two fundamental super-resolution approaches- learning fr...

2013 337 citations

Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering

Mikhail Belkin , Partha Niyogi

Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically moti...

2002 The MIT Press eBooks 4494 citations

A new optimizer using particle swarm theory

R.C. Eberhart , James Kennedy

The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently develope...

2002 14646 citations

Publication Info

Year: 2021
Type: article
Volume: 3
Issue: 3
Pages: 218-229
Citations: 2052
Access: Closed

External Links

View on DOI.org

Social Impact

Altmetric

Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

2052

OpenAlex

Cite This

APA Style

                            
                                
                                    Lu Lu, 
                                
                                    Pengzhan Jin, 
                                
                                    Guofei Pang
                                
                                et al.
                            
                            (2021). 
                            Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. 
                            Nature Machine Intelligence
                            , 3
                            (3)
                            , 218-229.
                            https://doi.org/10.1038/s42256-021-00302-5
                        

Identifiers

DOI: 10.1038/s42256-021-00302-5