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Found 5 papers, 1 papers with code
Cell division in deep material networks applied to multiscale strain localization modeling
Despite the increasing importance of strain localization modeling (e. g., failure analysis) in computer-aided engineering, there is a lack of effective approaches to capturing relevant material behaviors consistently across multiple length scales.
Machine learning for metal additive manufacturing: Predicting temperature and melt pool fluid dynamics using physics-informed neural networks
The recent explosion of machine learning (ML) and artificial intelligence (AI) shows great potential in the breakthrough of metal additive manufacturing (AM) process modeling.
Intelligent multiscale simulation based on process-guided composite database
In the paper, we present an integrated data-driven modeling framework based on process modeling, material homogenization, mechanistic machine learning, and concurrent multiscale simulation.
Deep material network with cohesive layers: Multi-stage training and interfacial failure analysis
A fundamental issue in multiscale materials modeling and design is the consideration of traction-separation behavior at the interface.
Exploring the 3D architectures of deep material network in data-driven multiscale mechanics
This paper extends the deep material network (DMN) proposed by Liu et al. (2019) to tackle general 3-dimensional (3D) problems with arbitrary material and geometric nonlinearities.
