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 …
DA-VEGAN: Differentiably Augmenting VAE-GAN for microstructure reconstruction from extremely small data sets
Microstructure reconstruction is an important and emerging field of research and an
essential foundation to improving inverse computational materials engineering (ICME) …
essential foundation to improving inverse computational materials engineering (ICME) …
Local–global decompositions for conditional microstructure generation
Conditional microstructure generation tools offer an important, inexpensive pathway to
constructing statistically diverse datasets for Integrated Computational Materials …
constructing statistically diverse datasets for Integrated Computational Materials …
Reconstructing microstructures from statistical descriptors using neural cellular automata
The problem of generating microstructures of complex materials in silico has been
approached from various directions including simulation, Markov, deep learning and …
approached from various directions including simulation, Markov, deep learning and …
Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling
Acquiring reliable microstructure datasets is a pivotal step toward the systematic design of
materials with the aid of integrated computational materials engineering (ICME) approaches …
materials with the aid of integrated computational materials engineering (ICME) approaches …
Inverse stochastic microstructure design
Abstract Inverse Microstructure Design problems are ubiquitous in materials science; for
example, property-driven microstructure design requires the inversion of a structure …
example, property-driven microstructure design requires the inversion of a structure …
[HTML][HTML] Predictive microstructure image generation using denoising diffusion probabilistic models
The rapid progress in artificial intelligence (AI) based image generation led to
groundbreaking achievements, like OpenAI's DALL-E 2, showcasing state-of-the-art …
groundbreaking achievements, like OpenAI's DALL-E 2, showcasing state-of-the-art …
Fast reconstruction of microstructures with ellipsoidal inclusions using analytical descriptors
Microstructure reconstruction is an important and emerging aspect of computational
materials engineering and multiscale modeling and simulation. Despite extensive research …
materials engineering and multiscale modeling and simulation. Despite extensive research …
Symmetric unisolvent equations for linear elasticity purely in stresses
In this work we introduce novel stress-only formulations of linear elasticity with special
attention to their approximate solution using weighted residual methods. We present four …
attention to their approximate solution using weighted residual methods. We present four …
Statistically conditioned polycrystal generation using denoising diffusion models
Synthetic microstructure generation algorithms have emerged as a key tool for enabling
large ICME and Materials Informatics efforts. In particular, statistically conditioned generative …
large ICME and Materials Informatics efforts. In particular, statistically conditioned generative …