[HTML][HTML] A review of artificial neural networks in the constitutive modeling of composite materials
Abstract Machine learning models are increasingly used in many engineering fields thanks
to the widespread digital data, growing computing power, and advanced algorithms. The …
to the widespread digital data, growing computing power, and advanced algorithms. The …
A state-of-the-art review on machine learning-based multiscale modeling, simulation, homogenization and design of materials
Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …
computational technology as well as engineering tools in material modeling and material …
[HTML][HTML] An FE–DMN method for the multiscale analysis of short fiber reinforced plastic components
In this work, we propose a fully coupled multiscale strategy for components made from short
fiber reinforced composites, where each Gauss point of the macroscopic finite element …
fiber reinforced composites, where each Gauss point of the macroscopic finite element …
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 …
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 …
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 …
[HTML][HTML] A space-time upscaling technique for modeling high-cycle fatigue-damage of short-fiber reinforced composites
Characterizing short-fiber reinforced polymers under high-cycle fatigue loading is a tedious
experimental task. To reduce the necessary experiments to a minimum, we introduce a …
experimental task. To reduce the necessary experiments to a minimum, we introduce a …
Training deep material networks to reproduce creep loading of short fiber-reinforced thermoplastics with an inelastically-informed strategy
Deep material networks (DMNs) are a recent multiscale technology which enable running
concurrent multiscale simulations on industrial scale with the help of powerful surrogate …
concurrent multiscale simulations on industrial scale with the help of powerful surrogate …