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07/05/2023
Error Approximation and Bias Correction in Dynamic Problems using a Recurrent Neural Network/Finite Element Hybrid Model
This work proposes a hybrid modeling framework based on recurrent neural...
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03/06/2023
Data-driven Modified Nodal Analysis Circuit Solver
This work introduces a novel data-driven modified nodal analysis (MNA) c...
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11/17/2022
Quadrupole Magnet Design based on Genetic Multi-Objective Optimization
This work suggests to optimize the geometry of a quadrupole magnet by me...
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06/29/2022
An hp-adaptive multi-element stochastic collocation method for surrogate modeling with information re-use
This paper introduces an hp-adaptive multi-element stochastic collocatio...
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05/18/2022
Power Module Heat Sink Design Optimization with Ensembles of Data-Driven Polynomial Chaos Surrogate Models
We consider the problem of optimizing the design of a heat sink used for...
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04/06/2022
Tensor train based isogeometric analysis for PDE approximation on parameter dependent geometries
This work develops a numerical solver based on the combination of isogeo...
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02/09/2022
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems
Constructing surrogate models for uncertainty quantification (UQ) on com...
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11/17/2021
A Neural Solver for Variational Problems on CAD Geometries with Application to Electric Machine Simulation
This work presents a deep learning-based framework for the solution of p...
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09/28/2021
Grassmannian diffusion maps based surrogate modeling via geometric harmonics
In this paper, a novel surrogate model based on the Grassmannian diffusi...
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07/30/2021
Three-Dimensional Data-Driven Magnetostatic Field Computation using Real-World Measurement Data
This paper presents a practical case study of a data-driven magnetostati...
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07/21/2021
Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models
In this work we introduce a manifold learning-based method for uncertain...
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06/29/2021
Tensor-train approximation of the chemical master equation and its application for parameter inference
In this work, we perform Bayesian inference tasks for the chemical maste...
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06/29/2021
Local field reconstruction from rotating coil measurements in particle accelerator magnets
In this paper a general approach to reconstruct three dimensional field ...
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08/19/2020
Data-Driven Solvers for Strongly Nonlinear Material Response
This work presents a data-driven magnetostatic finite-element solver tha...
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02/10/2020
Magnetic Field Simulation with Data-Driven Material Modeling
This paper developes a data-driven magnetostatic finite-element (FE) sol...
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12/16/2019
Robust Adaptive Least Squares Polynomial Chaos Expansions in High-Frequency Applications
We present an algorithm for computing sparse, least squares-based polyno...
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11/19/2019
Adaptive Sparse Polynomial Chaos Expansions via Leja Interpolation
This work suggests an interpolation-based stochastic collocation method ...
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04/16/2019
Approximation and Uncertainty Quantification of Stochastic Systems with Arbitrary Input Distributions using Weighted Leja Interpolation
Approximation and uncertainty quantification methods based on Lagrange i...
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07/19/2018
Uncertainty quantification for an optical grating coupler with an adjoint-based Leja adaptive collocation method
This paper addresses uncertainties arising in the nano-scale fabrication...
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12/19/2017
Assessing the Performance of Leja and Clenshaw-Curtis Collocation for Computational Electromagnetics with Random Input Data
We consider the problem of quantifying uncertainty regarding the output ...
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12/19/2017
