39 citations · 291 across the 31 of their papers we have counts for
31 papers
Maximum-Projection-Based Bayesian Optimization Utilizing Sensitivity Analysis for High-Efficiency Radial Turbine Design with Scarce Data
Eric Diehl, Adem Tosun, Dimitrios Loukrezis
We propose a data-efficient workflow to optimize the efficiency of a radial turbine design under a strict budget of high-fidelity computational fluid dynamics simulations. Assuming…
Multivariate Sensitivity Analysis of Electric Machine Efficiency Maps and Profiles Under Design Uncertainty
Aylar Partovizadeh, Sebastian Schöps, Dimitrios Loukrezis
This work introduces the use of multivariate global sensitivity analysis for assessing the impact of uncertain electric machine design parameters on efficiency maps and profiles. C…
Conformalized Polynomial Chaos Expansion for Uncertainty-aware Surrogate Modeling
Dimitrios Loukrezis, Dimitris G. Giovanis
This work introduces a method to equip data-driven polynomial chaos expansion surrogate models with intervals that quantify the predictive uncertainty of the surrogate. To that end…
Multi-patch isogeometric neural solver for partial differential equations on computer-aided design domains
Moritz von Tresckow, Ion Gabriel Ion, Dimitrios Loukrezis
This work develops a computational framework that combines physics-informed neural networks with multi-patch isogeometric analysis to solve partial differential equations on comple…
Fourier-enhanced reduced-order surrogate modeling for uncertainty quantification in electric machine design
Aylar Partovizadeh, Sebastian Schöps, Dimitrios Loukrezis
This work proposes a data-driven surrogate modeling framework for cost-effectively inferring the torque of a permanent magnet synchronous machine under geometric design variations.…
Polynomial Chaos Expansions on Principal Geodesic Grassmannian Submanifolds for Surrogate Modeling and Uncertainty Quantification
Dimitris G. Giovanis, Dimitrios Loukrezis, Ioannis G. Kevrekidis +1
In this work we introduce a manifold learning-based surrogate modeling framework for uncertainty quantification in high-dimensional stochastic systems. Our first goal is to perform…
Multivariate sensitivity-adaptive polynomial chaos expansion for high-dimensional surrogate modeling and uncertainty quantification
Dimitrios Loukrezis, Eric Diehl, Herbert De Gersem
This work develops a novel basis-adaptive method for constructing anisotropic polynomial chaos expansions of multidimensional (vector-valued, multi-output) model responses. The ada…
Error Approximation and Bias Correction in Dynamic Problems using a Recurrent Neural Network/Finite Element Hybrid Model
Moritz von Tresckow, Herbert De Gersem, Dimitrios Loukrezis
This work proposes a hybrid modeling framework based on recurrent neural networks (RNNs) and the finite element (FE) method to approximate model discrepancies in time dependent, mu…
UQpy v4.1: Uncertainty Quantification with Python
Dimitrios Tsapetis, Michael D. Shields, Dimitris G. Giovanis +9
This paper presents the latest improvements introduced in Version 4 of the UQpy, Uncertainty Quantification with Python, library. In the latest version, the code was restructured t…
Data-driven Modified Nodal Analysis Circuit Solver
Armin Galetzka, Dimitrios Loukrezis, Herbert De Gersem
This work introduces a novel data-driven modified nodal analysis (MNA) circuit solver. The solver is capable of handling circuit problems featuring elements for which solely measur…
Quadrupole Magnet Design based on Genetic Multi-Objective Optimization
Eric Diehl, Moritz von Tresckow, Lou Scholtissek +4
This work suggests to optimize the geometry of a quadrupole magnet by means of a genetic algorithm adapted to solve multi-objective optimization problems. To that end, a non-domina…
An -adaptive multi-element stochastic collocation method for surrogate modeling with information re-use
Armin Galetzka, Dimitrios Loukrezis, Niklas Georg +2
This paper introduces an -adaptive multi-element stochastic collocation method, which additionally allows to re-use existing model evaluations during either - or -refinem…
Power Module Heat Sink Design Optimization with Ensembles of Data-Driven Polynomial Chaos Surrogate Models
Dimitrios Loukrezis, Herbert De Gersem
We consider the problem of optimizing the design of a heat sink used for cooling an insulated gate bipolar transistor (IGBT) power module. The thermal behavior of the heat sink is…
Tensor train based isogeometric analysis for PDE approximation on parameter dependent geometries
Ion Gabriel Ion, Dimitrios Loukrezis, Herbert De Gersem
This work develops a numerical solver based on the combination of isogeometric analysis (IGA) and the tensor train (TT) decomposition for the approximation of partial differential…
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…
A Neural Solver for Variational Problems on CAD Geometries with Application to Electric Machine Simulation
Moritz von Tresckow, Stefan Kurz, Herbert De Gersem +1
This work presents a deep learning-based framework for the solution of partial differential equations on complex computational domains described with computer-aided design tools. T…
Grassmannian diffusion maps based surrogate modeling via geometric harmonics
Ketson R. M. dos Santos, Dimitrios G. Giovanis, Katiana Kontolati +2
In this paper, a novel surrogate model based on the Grassmannian diffusion maps (GDMaps) and utilizing geometric harmonics is developed for predicting the response of engineering s…
Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models
Katiana Kontolati, Dimitrios Loukrezis, Ketson R. M. dos Santos +2
In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to ide…
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…
Three-Dimensional Data-Driven Magnetostatic Field Computation using Real-World Measurement Data
Armin Galetzka, Dimitrios Loukrezis, Herbert De Gersem
This paper presents a practical case study of a data-driven magnetostatic finite element solver applied to a real-world three-dimensional problem. Instead of using a hard-coded phe…
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…