164 citations · 232 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…
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…
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…
From ductile damage to unilateral contact via point-wise implicit discontinuity at the infinitesimal element level
Alireza Daneshyar, Leon Herrmann, Stefan Kollmannsberger
Ductile damage models and cohesive laws incorporate the material plasticity entailing the growth of irrecoverable deformations even after complete failure. This unrealistic growth…
Transfer Learning Enhanced Full Waveform Inversion
Stefan Kollmannsberger, Divya Singh, Leon Herrmann
We propose a way to favorably employ neural networks in the field of non-destructive testing using Full Waveform Inversion (FWI). The presented methodology discretizes the unknown…
On the Use of Neural Networks for Full Waveform Inversion
Leon Herrmann, Tim Bürchner, Felix Dietrich +1
Neural networks have recently gained attention in solving inverse problems. One prominent methodology are Physics-Informed Neural Networks (PINNs) which can solve both forward and…