Modeling process-structure-property relationships for additive manufacturing
This paper presents our latest work on comprehensive modeling of process-structure-
property relationships for additive manufacturing (AM) materials, including using data …
property relationships for additive manufacturing (AM) materials, including using data …
Self-consistent clustering analysis: an efficient multi-scale scheme for inelastic heterogeneous materials
The discovery of efficient and accurate descriptions for the macroscopic behavior of
materials with complex microstructure is an outstanding challenge in mechanics of …
materials with complex microstructure is an outstanding challenge in mechanics of …
A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
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 …
deep material network, is developed based on mechanistic homogenization theory of …
Data-driven multi-scale multi-physics models to derive process–structure–property relationships for additive manufacturing
Additive manufacturing (AM) possesses appealing potential for manipulating material
compositions, structures and properties in end-use products with arbitrary shapes without …
compositions, structures and properties in end-use products with arbitrary shapes without …
Microstructural material database for self-consistent clustering analysis of elastoplastic strain softening materials
Multiscale modeling of heterogeneous material undergoing strain softening poses
computational challenges for localization of the microstructure, material instability in the …
computational challenges for localization of the microstructure, material instability in the …
An overview on uncertainty quantification and probabilistic learning on manifolds in multiscale mechanics of materials
C Soize - Mathematics and Mechanics of Complex Systems, 2023 - msp.org
An overview of the author's works, many of which were carried out in collaboration, is
presented. The first part concerns the quantification of uncertainties for complex engineering …
presented. The first part concerns the quantification of uncertainties for complex engineering …
MAP123-EP: A mechanistic-based data-driven approach for numerical elastoplastic analysis
In this paper, a mechanistic-based data-driven approach, MAP123-EP, is proposed for
numerical analysis of elastoplastic materials. In this method, stress-update is driven by a set …
numerical analysis of elastoplastic materials. In this method, stress-update is driven by a set …
Data-driven self-consistent clustering analysis of heterogeneous materials with crystal plasticity
To analyze complex, heterogeneous materials, a fast and accurate method is needed. This
means going beyond the classical finite element method, in a search for the ability to …
means going beyond the classical finite element method, in a search for the ability to …
Predicting the mechanical properties of biopolymer gels using neural networks trained on discrete fiber network data
Biopolymer gels, such as those made out of fibrin or collagen, are widely used in tissue
engineering applications and biomedical research. Moreover, fibrin naturally assembles into …
engineering applications and biomedical research. Moreover, fibrin naturally assembles into …
An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials
X Li, Z Li, Y Chen, C Zhang - European Journal of Mechanics-A/Solids, 2023 - Elsevier
Data-driven and machine-learning based approaches provide a highly compatible and
efficient fundamentals for the mechanical constitutive modeling of engineering materials. In …
efficient fundamentals for the mechanical constitutive modeling of engineering materials. In …