In this work we develop a dynamically adaptive sparse grids (SG) method for quasi-optimal
interpolation of multidimensional analytic functions defined over a product of one …

We consider general strategy for hierarchical multidimensional interpolation based on
sparse grids, where the interpolation nodes and locally supported basis functions are …

We present and analyze a novel sparse polynomial technique for the simultaneous
approximation of parameterized partial differential equations (PDEs) with deterministic and …

The management of wastewater treatment plant (WWTP) and the assessment of uncertainty
in its design are crucial from an environmental engineering perspective. One of the key …

In this work we develop a dynamically adaptive sparse grids (SG) method for quasi-optimal
interpolation of multidimensional analytic functions defined over a product of one …

We consider the estimation of parameter-dependent statistics of functional outputs of steady-
state convection–diffusion–reaction equations with parametrized random and deterministic …

This work studies sparse reconstruction techniques for approximating solutions of high-
dimensional parametric PDEs. Such problems are relevant to mathematical modeling in …

Over seventy years ago, Richard Bellman coined the term the curse of dimensionality to
describe phenomena and computational challenges that arise in high dimensions. These …

Learning high-or infinite-dimensional functions from limited samples is a key task in
Computational Science and Engineering (CSE). For example, in Uncertainty Quantification …

In the present paper, the uncertainty analysis for the activated sludge part in the wastewater
treatment plant (WWTP) was done using stochastic differential equation (SDE) equations …