17 citations · 17 across the 1 of their papers we have counts for
6 papers
Cell division in deep material networks applied to multiscale strain localization modeling
Zeliang Liu
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 relev…
Machine learning for metal additive manufacturing: Predicting temperature and melt pool fluid dynamics using physics-informed neural networks
Qiming Zhu, Zeliang Liu, Jinhui Yan
The recent explosion of machine learning (ML) and artificial intelligence (AI) shows great potential in the breakthrough of metal additive manufacturing (AM) process modeling. Howe…
Intelligent multiscale simulation based on process-guided composite database
Zeliang Liu, Haoyan Wei, Tianyu Huang +1
In the paper, we present an integrated data-driven modeling framework based on process modeling, material homogenization, mechanistic machine learning, and concurrent multiscale si…
Deep material network with cohesive layers: Multi-stage training and interfacial failure analysis
Zeliang Liu
A fundamental issue in multiscale materials modeling and design is the consideration of traction-separation behavior at the interface. By enriching the deep material network (DMN)…
Exploring the 3D architectures of deep material network in data-driven multiscale mechanics
Zeliang Liu, C. T. Wu
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.…
A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
Zeliang Liu, C. T. Wu, M. Koishi
In this paper, a new data-driven multiscale material modeling method, which we refer to as deep material network, is developed based on mechanistic homogenization theory of represe…