Documents published in Scipedia

• Machine Learning Assisted Mesh Adaptation for Geophysical Fluid Dynamics

  S. Li, E. Johnson, J. Wallwork, S. Kramer, M. Piggott

  admos2023.

  
  

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• Sparse recovery problem in a hierarchical Bayesian framework

  M. Pragliola, D. Calvetti, E. Somersalo

  admos2023.

  
  

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• Dimension Reduction of Dynamic Superresolution and Application to Cell Tracking in PET

  A. Schlüter, M. Holler, B. Wirth

  admos2023.

  
  

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• Towards patient-specific modelling of Atherosclerotic Arterial Sections

  S. Gahima, P. Díez, M. Stefani, J. Rodríguez, A. García-González

  admos2023.

  
  

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• Digital Volume Correlation techniques for patient-specific simulation of vertebrae with metastasis

  L. Person, F. Hild, E. Nadal, J. Roderas, O. Allix

  admos2023.

  
  

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• Clustering-based Parametric Surrogate Modeling of Vibroacoustic Problems Assisted by Neural Networks and Active Subspace Method

  H. Sreekumar, L. Outzen, U. Römer, S. Langer

  admos2023.

  
  

  Abstract

  This contribution presents a combined framework to perform parametric surrogate modeling of vibroacoustic problems that enables efficient training of large-scale problems. The proposed framework combines the active subspace method to perform dimensionality reduction of high-dimensional problems and thereafter a clustering-based approach within the identified active subspace region to yield smaller training clusters. Finally, a trained neural network assists the cluster classification task for any desired parameter point so as to query the parametric system response during the online phase.

  Abstract
  This contribution presents a combined framework to perform parametric surrogate modeling of vibroacoustic problems that enables efficient training of large-scale problems. [...]

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• Mesh- and model adaptivity for elasto-plastic mean-field and full-field homogenization based on downwind and upwind approximations

  A. Tchomgue-Simeu, R. Mahnken

  admos2023.

  
  

  Abstract

  Materials such as composites are heterogeneous at the micro-scale, where several constituents with different material properties can be distinguished like elastic inclusions and the elasto-plastic matrix with isotropic hardening. One has to deal with these heterogeneities on the micro-scale and then perform a scale transition to obtain the overall behavior on the macro-scale, which is often referred to as homogenization. The present contribution deals with the combination of numerically inexpensive mean-field and numerically expensive full-field homogenization methods in elasto-plasticity coupled to adaptive finite element method (FEM) which takes into account error generation and error transport at each time step on the macro-scale. The proposed adaptive procedure is driven by a goal-oriented a posteriori error estimator based on duality techniques. The main difficulty of duality techniques in the literature is that the backwards-in-time al gorithm has a high demand on memory capacity since additional memory is required to store the primary solutions computed over all time steps. To this end, several down wind and upwind approximations are introduced for an elasto-plastic primal problem by means of jump terms [1]. Therefore, from a computational point of view, the forwards-in time duality problem is very attractive. A numerical example illustrates the effectiveness of the proposed adaptive approach based on forwards-in-time method in comparison to backwards-in-time method.

  Abstract
  Materials such as composites are heterogeneous at the micro-scale, where several constituents with different material properties can be distinguished like elastic inclusions [...]

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• Multiscale Finite Element approaches: error estimations and adaptivity for an enriched variant

  F. Legoll

  admos2023.

  
  

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• Design Allowables generation by High Fidelity Models for Interlaminar damage propagation based on Uncertainty Quantification

  J. Ninyerola-Gavaldà, A. Sasikumar, A. Turon-Travesa

  admos2023.

  
  

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• Runge Kutta (ELDIRK) methods for embedding of low order implicit time integration schemes for goal oriented global error estimation

  R. Mahnken

  admos2023.

  
  

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