15 citations · 31 across the 7 of their papers we have counts for
7 papers
Functional Autoencoder for Smoothing and Representation Learning
Sidi Wu, Cédric Beaulac, Jiguo Cao
A common pipeline in functional data analysis is to first convert the discretely observed data to smooth functions, and then represent the functions by a finite-dimensional vector…
Solving parametric elliptic interface problems via interfaced operator network
Sidi Wu, Aiqing Zhu, Yifa Tang +1
Learning operators mapping between infinite-dimensional Banach spaces via neural networks has attracted a considerable amount of attention in recent years. In this paper, we propos…
Error analysis based on inverse modified differential equations for discovery of dynamics using linear multistep methods and deep learning
Aiqing Zhu, Sidi Wu, Yifa Tang
Along with the practical success of the discovery of dynamics using deep learning, the theoretical analysis of this approach has attracted increasing attention. Prior works have es…
Neural Networks for Scalar Input and Functional Output
Sidi Wu, Cédric Beaulac, Jiguo Cao
The regression of a functional response on a set of scalar predictors can be a challenging task, especially if there is a large number of predictors, or the relationship between th…
Neuroimaging Feature Extraction using a Neural Network Classifier for Imaging Genetics
Cédric Beaulac, Sidi Wu, Erin Gibson +5
A major issue in the association of genes to neuroimaging phenotypes is the high dimension of both genetic data and neuroimaging data. In this article, we tackle the latter problem…
Convergence of physics-informed neural networks applied to linear second-order elliptic interface problems
Sidi Wu, Aiqing Zhu, Yifa Tang +1
With the remarkable empirical success of neural networks across diverse scientific disciplines, rigorous error and convergence analysis are also being developed and enriched. Howev…
FuncNN: An R Package to Fit Deep Neural Networks Using Generalized Input Spaces
Barinder Thind, Sidi Wu, Richard Groenewald +1
Neural networks have excelled at regression and classification problems when the input space consists of scalar variables. As a result of this proficiency, several popular packages…