Herein, an extensive review of the design of titanium alloys with low elastic modulus as
implant materials is provided. Due to the high specific strength, low elastic modulus, fatigue …

A neural-network machine called “βLow” enables a high-throughput recommendation for
new β titanium alloys with Young's moduli lower than 50 GPa. The machine was trained by …

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 …

We investigate deep material networks (DMNs), recently introduced by Liu et al.[Comput.
Method Appl. M., vol. 345, pp. 1138–1168, 2019], from the viewpoint of classical …

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 …

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 …

Deep material networks (DMNs) are a recent multiscale technology which enable running
concurrent multiscale simulations on industrial scale with the help of powerful surrogate …

We extend the FE-DMN method to fully coupled thermomechanical two-scale simulations of
composite materials. In particular, every Gauss point of the macroscopic finite element …

The isothermal tensile test of medium carbon steel material was conducted at deformation
temperatures varying from 650 to 950∘ C with an interval of 100∘ C and strain rates ranging …

Thermoplastic elastomers are materials widely used in engineering applications due to their
excellent performance to absorb mechanical vibrations and to reduce impact forces …