164 citations · 187 across the 9 of their papers we have counts for
10 papers
Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset
Marchellino Ghorayeb, Christiane Rößler, Horst-Michael Ludwig +2
Routine cement performance characterization provides continuous quality control data, but its information content for performance inference and transferability across independent p…
Lightweight return-mapping surrogates for multiscale plasticity: a practical guide
Alireza Daneshyar, Leon Herrmann, Stefan Kollmannsberger
This paper presents a practical guide to building lightweight neural-network surrogates for the plastic return-mapping process in concurrent multiscale (FE2) simulations. Rather th…
A Memory Efficient Adjoint Method to Enable Billion Parameter Optimization on a Single GPU in Dynamic Problems
Leon Herrmann, Tim Bürchner, László Kudela +1
Dynamic optimization is currently limited by sensitivity computations that require information from full forward and adjoint wave fields. Since the forward and adjoint solutions ar…
Full-waveform inversion via the scaled boundary finite element method
Alireza Daneshyar, Stefan Kollmannsberger
We begin by addressing the time-domain full-waveform inversion using the adjoint method. Next, we derive the scaled boundary semi-weak form of the scalar wave equation in heterogen…
A plastic damage model with mixed isotropic-kinematic hardening for low-cycle fatigue in 7020 aluminum
Alireza Daneshyar, Dorina Siebert, Christina Radlbeck +1
The paper at hand presents an in-depth investigation into the fatigue behavior of the high-strength aluminum alloy EN AW-7020 T6 using both experimental and numerical approaches. T…
Accelerating Full Waveform Inversion By Transfer Learning
Divya Shyam Singh, Leon Herrmann, Qing Sun +3
Full waveform inversion (FWI) is a powerful tool for reconstructing material fields based on sparsely measured data obtained by wave propagation. For specific problems, discretizin…
Neural Networks for Generating Better Local Optima in Topology Optimization
Leon Herrmann, Ole Sigmund, Viola Muning Li +2
Neural networks have recently been employed as material discretizations within adjoint optimization frameworks for inverse problems and topology optimization. While advantageous re…
The Discontinuous Strain Method: accurately representing fatigue and failure
Leon Herrmann, Alireza Daneshyar, Stefan Kollmannsberger
Fatigue simulation requires accurate modeling of unloading and reloading. However, classical ductile damage models treat deformations after complete failure as irrecoverable -- whi…
On the radial discretization of finite element spaces in the scaled boundary finite element method
Alireza Daneshyar, Stefan Kollmannsberger
The scaled boundary finite element method is known for its capability in reproducing highly-detailed solution fields. This, however, is only attainable in those cases where analyti…
Deep Learning in Deterministic Computational Mechanics
Leon Herrmann, Stefan Kollmannsberger
The rapid growth of deep learning research, including within the field of computational mechanics, has resulted in an extensive and diverse body of literature. To help researchers…