This work proposes a hybrid modeling framework based on recurrent neural...
This work introduces a novel data-driven modified nodal analysis (MNA)
c...
This work suggests to optimize the geometry of a quadrupole magnet by me...
This paper introduces an hp-adaptive multi-element stochastic collocatio...
We consider the problem of optimizing the design of a heat sink used for...
This work develops a numerical solver based on the combination of
isogeo...
Constructing surrogate models for uncertainty quantification (UQ) on com...
This work presents a deep learning-based framework for the solution of
p...
In this paper, a novel surrogate model based on the Grassmannian diffusi...
This paper presents a practical case study of a data-driven magnetostati...
In this work we introduce a manifold learning-based method for uncertain...
In this work, we perform Bayesian inference tasks for the chemical maste...
In this paper a general approach to reconstruct three dimensional field
...
This work presents a data-driven magnetostatic finite-element solver tha...
This paper developes a data-driven magnetostatic finite-element (FE) sol...
We present an algorithm for computing sparse, least squares-based polyno...
This work suggests an interpolation-based stochastic collocation method ...
Approximation and uncertainty quantification methods based on Lagrange
i...
This paper addresses uncertainties arising in the nano-scale fabrication...
We consider the problem of quantifying uncertainty regarding the output ...
We consider the problem of approximating the output of a parametric
elec...