Deep learning in computational mechanics: a review
L Herrmann, S Kollmannsberger - Computational Mechanics, 2024 - Springer
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 …
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …
Efficient multiscale modeling of heterogeneous materials using deep neural networks
Material modeling using modern numerical methods accelerates the design process and
reduces the costs of developing new products. However, for multiscale modeling of …
reduces the costs of developing new products. However, for multiscale modeling of …
Spiking neural networks for nonlinear regression
A Henkes, JK Eshraghian… - Royal Society Open …, 2024 - royalsocietypublishing.org
Spiking neural networks (SNN), also often referred to as the third generation of neural
networks, carry the potential for a massive reduction in memory and energy consumption …
networks, carry the potential for a massive reduction in memory and energy consumption …
Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics
Multiscale simulations are demanding in terms of computational resources. In the context of
continuum micromechanics, the multiscale problem arises from the need of inferring …
continuum micromechanics, the multiscale problem arises from the need of inferring …
Two-stage 2D-to-3D reconstruction of realistic microstructures: Implementation and numerical validation by effective properties
Realistic microscale domains are an essential step towards making modern multiscale
simulations more applicable to computational materials engineering. For this purpose, 3D …
simulations more applicable to computational materials engineering. For this purpose, 3D …
Probabilistic failure mechanisms via Monte Carlo simulations of complex microstructures
A probabilistic approach to phase-field brittle and ductile fracture with random material and
geometric properties is proposed within this work. In the macroscopic failure mechanics …
geometric properties is proposed within this work. In the macroscopic failure mechanics …
Concurrent multiscale simulations of nonlinear random materials using probabilistic learning
This work is concerned with the construction of statistical surrogates for concurrent
multiscale modeling in structures comprising nonlinear random materials. The development …
multiscale modeling in structures comprising nonlinear random materials. The development …
A machine‐learning supported multi‐scale LBM‐TPM model of unsaturated, anisotropic, and deformable porous materials
The purpose of this paper is to investigate the utilization of artificial neural networks (ANNs)
in learning models that address the nonlinear anisotropic flow and hysteresis retention …
in learning models that address the nonlinear anisotropic flow and hysteresis retention …
Deep Learning in Deterministic Computational Mechanics
L Herrmann, S Kollmannsberger - arXiv preprint arXiv:2309.15421, 2023 - arxiv.org
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 …
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …
Divergence-free neural operators for stress field modeling in polycrystalline materials
The purpose of the current work is the development and comparison of Fourier neural
operators (FNOs) for surrogate modeling of the quasi-static mechanical response of …
operators (FNOs) for surrogate modeling of the quasi-static mechanical response of …