As high-dimensional and high-frequency data are being collected on a large scale, the
development of new statistical models is being pushed forward. Functional data analysis …

We review recent advances in Object Oriented Spatial Statistics, a system of ideas,
algorithms and methods that allows the analysis of high dimensional and complex data …

Physics-informed neural networks (PINNs) are a promising approach that combines the
power of neural networks with the interpretability of physical modeling. PINNs have shown …

This work gives an overview of an innovative class of methods for the analysis of spatial and
of functional data observed over complicated two‐dimensional domains. This class is based …

In this work, we provide a computational study of the effects of carotid endarterectomy (CEA)
on the fluid-dynamics at internal carotid bifurcations. We perform numerical simulations in …

We propose a method for the analysis of functional data with complex dependencies, such
as spatially dependent curves or time dependent surfaces, over highly textured domains …

We propose a regression model for data spatially distributed over general two-dimensional
Riemannian manifolds. This is a generalized additive model with a roughness penalty term …

Physics-informed machine learning combines the expressiveness of data-based
approaches with the interpretability of physical models. In this context, we consider a …

In this article, we introduce a new class of nonparametric regression models, called
generalized spatially varying coefficient models (GSVCMs), for data distributed over …

We study the consistency of the estimator in a spatial regression with partial differential
equation (PDE) regularization. This new smoothing technique allows us to accurately …