[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 review of predictive nonlinear theories for multiscale modeling of heterogeneous materials
Since the beginning of the industrial age, material performance and design have been in the
midst of innovation of many disruptive technologies. Today's electronics, space, medical …
midst of innovation of many disruptive technologies. Today's electronics, space, medical …
[HTML][HTML] Holistic computational design within additive manufacturing through topology optimization combined with multiphysics multi-scale materials and process …
Additive manufacturing (AM) processes have proven to be a perfect match for topology
optimization (TO), as they are able to realize sophisticated geometries in a unique layer-by …
optimization (TO), as they are able to realize sophisticated geometries in a unique layer-by …
Smart constitutive laws: Inelastic homogenization through machine learning
Homogenizing the constitutive response of materials with nonlinear and history-dependent
behavior at the microscale is particularly challenging. In this case, the only option is …
behavior at the microscale is particularly challenging. In this case, the only option is …
A comparative review of multiscale models for effective properties of nano-and micro-composites
Modelling and simulation techniques are now considered an essential practice for the
materials industry. In order to gain insight into factors that can affect the final properties of a …
materials industry. In order to gain insight into factors that can affect the final properties of a …
Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks
Hierarchical computational methods for multiscale mechanics such as the FE 2 and FE-FFT
methods are generally accompanied by high computational costs. Data-driven approaches …
methods are generally accompanied by high computational costs. Data-driven approaches …
Homogenization of composites with extended general interfaces: comprehensive review and unified modeling
Interphase regions that form in heterogeneous materials through various underlying
mechanisms such as poor mechanical or chemical adherence, roughness, and coating, play …
mechanisms such as poor mechanical or chemical adherence, roughness, and coating, play …
Model-data-driven constitutive responses: Application to a multiscale computational framework
Computational multiscale methods for analyzing and deriving constitutive responses have
been used as a tool in engineering problems because of their ability to combine information …
been used as a tool in engineering problems because of their ability to combine information …
Learning hyperelastic anisotropy from data via a tensor basis neural network
Anisotropy in the mechanical response of materials with microstructure is common and yet is
difficult to assess and model. To construct accurate response models given only stress …
difficult to assess and model. To construct accurate response models given only stress …
Machine learning-based multiscale framework for mechanical behavior of nano-crystalline structures
In this paper, a computational atomistic-continuum multiscale framework is developed based
on the machine learning (ML) architecture to capture the nonlinear behavior of nano …
on the machine learning (ML) architecture to capture the nonlinear behavior of nano …