Titlebook: Machine Learning Applied to Composite Materials; Vinod Kushvaha,M. R. Sanjay,Suchart Siengchin Book 2022 The Editor(s) (if applicable) and

朝圣者

燒烤

Applications of Machine Learning in the Field of Polymer Composites,al yet feasible product. Modeling the complex relationships between the various governing factors is extremely strenuous and generally requires the development of a mathematical tool. This has motivated researchers to look for time saving and less expensive computational techniques. Machine learning

neoplasm

LURE

Vertebra

Generosity

Application of Machine Learning in Determining the Mechanical Properties of Materials,st range of applications. With evaluation in material characterization techniques large amounts of material data are obtained through experiments and simulations. Even in some cases theoretical concepts cannot be applicable to these data. With increase in material data, application of machine learni

aesthetician

Machine Learning Prediction for the Mechanical Properties of Lightweight Composite Materials, and high specific mechanical properties make composite materials a successor to those conventional metal alloys. Another benefit of composite materials is that their mechanical properties can be designed to meet the criteria for certain engineering applications. However, the mechanical properties o

社團(tuán)

Ballistic Performance of Bi-layer Graphene: Artificial Neural Network Based Molecular Dynamics Simuhe computationally expensive nature of large scale MD simulations frequently hinders a thorough understanding of material characterization. To mitigate this lacuna we demonstrated the successful integration of MD simulation with the artificial neural network (ANN). In this regard, the considered inp

窩轉(zhuǎn)脊椎動(dòng)物

ULCER

Estimating Axial Load Capacity of Concrete-Filled Double-Skin Steel Tubular Columns of Multiple Shanner and outer steel tubes. The confined concrete behavior in these composite columns is affected by the shape of the inner and outer steel tubes. A new hybrid approach using genetic algorithm (GA)-optimized artificial neural networks (ANNs) is proposed in this study to estimate the axial load capac