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 …
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 …
Denoising diffusion probabilistic models for generative alloy design
Inverse material design is an extremely challenging optimization task made difficult by, in
part, the highly nonlinear relationship linking performance with composition. Quantitative …
part, the highly nonlinear relationship linking performance with composition. Quantitative …
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 …
Digital polycrystalline microstructure generation using diffusion probabilistic models
Accurate micromechanical simulation of polycrystalline materials requires a realistic digital
representation of the grain scale microstructure. This work demonstrates the use of a …
representation of the grain scale microstructure. This work demonstrates the use of a …
Stochastic reconstruction of heterogeneous microstructure combining sliced Wasserstein distance and gradient optimization
Z Ma, Q Teng, P Yan, X Wu, X He - Acta Materialia, 2024 - Elsevier
Computational reconstruction methods play an important role in integrated computational
materials engineering, providing an efficient and inexpensive way for multi-modal and multi …
materials engineering, providing an efficient and inexpensive way for multi-modal and multi …
Active learning for the design of polycrystalline textures using conditional normalizing flows
Generative modeling has opened new avenues for solving previously intractable materials
design problems. However, these new opportunities are accompanied by a drastic increase …
design problems. However, these new opportunities are accompanied by a drastic increase …
Constructing boundary-identical microstructures via guided diffusion for fast multiscale topology optimization
Hierarchical structures exhibit critical features across multiple scales. However, designing
multiscale structures demands significant computational resources, and ensuring …
multiscale structures demands significant computational resources, and ensuring …
MICRO2D: A Large, Statistically Diverse, Heterogeneous Microstructure Dataset
The availability of large, diverse datasets has enabled transformative advances in a wide
variety of technical fields by unlocking data scientific and machine learning techniques. In …
variety of technical fields by unlocking data scientific and machine learning techniques. In …
Self-supervised feature distillation and design of experiments for efficient training of micromechanical deep learning surrogates
Machine learning surrogate emulators are needed in engineering design and optimization
tasks to rapidly emulate computationally expensive physics-based models. In …
tasks to rapidly emulate computationally expensive physics-based models. In …