A review on data-driven constitutive laws for solids
This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …
surrogate, or emulate constitutive laws that describe the path-independent and path …
Neural networks for constitutive modeling: From universal function approximators to advanced models and the integration of physics
Analyzing and modeling the constitutive behavior of materials is a core area in materials
sciences and a prerequisite for conducting numerical simulations in which the material …
sciences and a prerequisite for conducting numerical simulations in which the material …
Microstructure-guided deep material network for rapid nonlinear material modeling and uncertainty quantification
Modeling nonlinear materials with arbitrary microstructures and loading paths is crucial in
structural analyses with heterogeneous materials with uncertainty. However, it is …
structural analyses with heterogeneous materials with uncertainty. However, it is …
[HTML][HTML] A multi-fidelity data-driven model for highly accurate and computationally efficient modeling of short fiber composites
HL Cheung, M Mirkhalaf - Composites Science and Technology, 2024 - Elsevier
To develop physics-based models and establish a structure–property relationship for short
fiber composites, there are a wide range of micro-structural properties to be considered. To …
fiber composites, there are a wide range of micro-structural properties to be considered. To …
Deep homogenization networks for elastic heterogeneous materials with two-and three-dimensional periodicity
We present a deep learning framework that leverages computational homogenization
expertise to predict the local stress field and homogenized moduli of heterogeneous …
expertise to predict the local stress field and homogenized moduli of heterogeneous …
Rapid inverse calibration of a multiscale model for the viscoplastic and creep behavior of short fiber-reinforced thermoplastics based on Deep Material Networks
In this work, we propose to use deep material networks (DMNs) as a surrogate model for full-
field computational homogenization to inversely identify material parameters of constitutive …
field computational homogenization to inversely identify material parameters of constitutive …
LS-DYNA machine learning–based multiscale method for nonlinear modeling of short fiber–reinforced composites
Short fiber–reinforced composites (SFRCs) are high-performance engineering materials for
lightweight structural applications in the automotive and electronics industries. Typically …
lightweight structural applications in the automotive and electronics industries. Typically …
[HTML][HTML] A probabilistic virtual process chain to quantify process-induced uncertainties in Sheet Molding Compounds
The manufacturing process of Sheet Molding Compound (SMC) influences the properties of
a component in a non-deterministic fashion. To predict this influence on the mechanical …
a component in a non-deterministic fashion. To predict this influence on the mechanical …
[HTML][HTML] Deep neural network homogenization of multiphysics behavior for periodic piezoelectric composites
We present a physically informed deep neural network homogenization theory for identifying
homogenized moduli and local electromechanical fields of periodic piezoelectric …
homogenized moduli and local electromechanical fields of periodic piezoelectric …
Deep material network via a quilting strategy: visualization for explainability and recursive training for improved accuracy
Recent developments integrating micromechanics and neural networks offer promising
paths for rapid predictions of the response of heterogeneous materials with similar accuracy …
paths for rapid predictions of the response of heterogeneous materials with similar accuracy …