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 …
Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering
In this work, a unified AI-framework named Hierarchical Deep Learning Neural Network
(HiDeNN) is proposed to solve challenging computational science and engineering …
(HiDeNN) is proposed to solve challenging computational science and engineering …
Integrated computational materials engineering for advanced automotive technology: with focus on life cycle of automotive body structure
Integrated computational materials engineering (ICME) is a simulation‐driven design
approach that employs multiscale‐multiphysics modeling and is based on the …
approach that employs multiscale‐multiphysics modeling and is based on the …
An integrated computational materials engineering framework to analyze the failure behaviors of carbon fiber reinforced polymer composites for lightweight vehicle …
A bottom-up multi-scale modeling approach is used to develop an Integrated Computational
Materials Engineering (ICME) framework for carbon fiber reinforced polymer (CFRP) …
Materials Engineering (ICME) framework for carbon fiber reinforced polymer (CFRP) …
[HTML][HTML] A deep learning convolutional neural network and multi-layer perceptron hybrid fusion model for predicting the mechanical properties of carbon fiber
Recently, deep learning methods have become one of the hottest topics in predicting
material properties, however, one bottleneck in current research is the simultaneous …
material properties, however, one bottleneck in current research is the simultaneous …
Numerical cross-scale optimization of homogenized composite laminates under impact loading
The complex spatial micro-and macrostructure layout cause an enormous difference in the
overall performance of composites. Cross-scale optimization is computationally very …
overall performance of composites. Cross-scale optimization is computationally very …
[HTML][HTML] Study on the interface toughening of particle/fibre reinforced epoxy composites with molecularly designed core–shell particles and various interface 3D …
N Thirunavukkarasu, HB Gunasekaran, S Peng… - Materials & Design, 2023 - Elsevier
Poor interface toughening reduces the utilisation of particle/fibre-reinforced thermoset
polymer composites in many engineering applications. Numerous studies have been …
polymer composites in many engineering applications. Numerous studies have been …
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 …
Phenomenological constitutive modeling of the non-linear loading-unloading response of UD fiber-reinforced polymers
H Fallahi, F Taheri-Behrooz - Composite Structures, 2022 - Elsevier
The matrix-dominated mechanical response of a unidirectional carbon/epoxy system is
studied through experiments and constitutive modeling. Experiments were performed on off …
studied through experiments and constitutive modeling. Experiments were performed on off …
GP+: a python library for kernel-based learning via Gaussian Processes
In this paper we introduce GP+, an open-source library for kernel-based learning via
Gaussian processes (GPs) which are powerful statistical models that are completely …
Gaussian processes (GPs) which are powerful statistical models that are completely …