20 citations · 48 across the 5 of their papers we have counts for
10 papers
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems
Katiana Kontolati, Dimitrios Loukrezis, Dimitris G. Giovanis +2
Constructing surrogate models for uncertainty quantification (UQ) on complex partial differential equations (PDEs) having inherently high-dimensional stoc…
Tensor-train approximation of the chemical master equation and its application for parameter inference
Ion Gabriel Ion, Christian Wildner, Dimitrios Loukrezis +2
In this work, we perform Bayesian inference tasks for the chemical master equation in the tensor-train format. The tensor-train approximation has been proven to be very efficient i…
Local field reconstruction from rotating coil measurements in particle accelerator magnets
Ion Gabriel Ion, Melvin Liebsch, Abele Simona +5
In this paper a general approach to reconstruct three dimensional field solutions in particle accelerator magnets from distributed magnetic measurements is presented. To exploit th…
Data-Driven Solvers for Strongly Nonlinear Material Response
Armin Galetzka, Dimitrios Loukrezis, Herbert De Gersem
This work presents a data-driven magnetostatic finite-element solver that is specifically well-suited to cope with strongly nonlinear material responses. The data-driven computing…
Magnetic Field Simulation with Data-Driven Material Modeling
Herbert De Gersem, Armin Galetzka, Ion Gabriel Ion +2
This paper developes a data-driven magnetostatic finite-element (FE) solver which directly exploits measured material data instead of a material curve constructed from it. The dist…
Robust Adaptive Least Squares Polynomial Chaos Expansions in High-Frequency Applications
Dimitrios Loukrezis, Armin Galetzka, Herbert De Gersem
We present an algorithm for computing sparse, least squares-based polynomial chaos expansions, incorporating both adaptive polynomial bases and sequential experimental designs. The…