[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 mechanics‐informed artificial neural network approach in data‐driven constitutive modeling
A mechanics‐informed artificial neural network approach for learning constitutive laws
governing complex, nonlinear, elastic materials from strain–stress data is proposed. The …
governing complex, nonlinear, elastic materials from strain–stress data is proposed. The …
A mechanics-informed deep learning framework for data-driven nonlinear viscoelasticity
A mechanics-informed, data-driven framework for learning the constitutive law of a nonlinear
viscoelastic material from stress–strain data using deep artificial neural networks (ANNs) is …
viscoelastic material from stress–strain data using deep artificial neural networks (ANNs) is …
Learning nonlinear constitutive laws using neural network models based on indirectly measurable data
Artificial neural network (ANN) models are used to learn the nonlinear constitutive laws
based on indirectly measurable data. The real input and output of the ANN model are …
based on indirectly measurable data. The real input and output of the ANN model are …
A mechanics-informed neural network framework for data-driven nonlinear viscoelasticity
View Video Presentation: https://doi. org/10.2514/6.2023-0949. vid A mechanics-informed,
data-driven framework for learning the constitutive law of a complex viscoelastic material …
data-driven framework for learning the constitutive law of a complex viscoelastic material …
Refined nonlinear micromechanical models using artificial neural networks for multiscale analysis of laminated composites subject to low-velocity impact
The parametric high fidelity generalized method of cells (PHFGMC) is an advanced
micromechanical method that can be used for the nonlinear and failure analysis of several …
micromechanical method that can be used for the nonlinear and failure analysis of several …
Tractable multiscale modeling with an embedded microscale surrogate
View Video Presentation: https://doi. org/10.2514/6.2021-1963. vid A neural network was
trained as a computationally efficient surrogate for a physics-based micromechanics model …
trained as a computationally efficient surrogate for a physics-based micromechanics model …
[HTML][HTML] Design of Shape Forming Elements for Architected Composites via Bayesian Optimization and Genetic Algorithms: A Concept Evaluation
This article presents the first use of shape forming elements (SFEs) to produce architected
composites from multiple materials in an extrusion process. Each SFE contains a matrix of …
composites from multiple materials in an extrusion process. Each SFE contains a matrix of …
A Robust Machine Learning Schema for Develo**, Maintaining, and Disseminating Machine Learning Models
BL Hearley, SM Arnold, J Stuckner - 2022 - ntrs.nasa.gov
Recent advances in the development of machine learning (ML) algorithms have enabled the
creation of predictive models that can improve decision making, decrease computational …
creation of predictive models that can improve decision making, decrease computational …
A Robust Schema for Machine Learning Data and Models Within the Granta MI Information Management System
View Video Presentation: https://doi. org/10.2514/6.2023-1266. vid Recent advances in the
development of machine learning (ML) algorithms have enabled the creation of predictive …
development of machine learning (ML) algorithms have enabled the creation of predictive …