Current trends in the progress of technology demand availability of materials resources
ahead of the advancing fronts of the application areas. During the last couple of decades …

Traditional strategies for designing new materials with targeted property including methods
such as trial and error, and experiences of domain experts, are time and cost consuming. In …

In this paper, the quality of a source of drinking water is assessed by measuring eight water
quality (WQ) parameters using 710 samples collected from a water-stressed region of India …

Neural networks are now a prominent feature of materials science with rapid progress in all
sectors of the subject. It is premature, however, to claim that the method is established …

Data-driven method utilizes machine learning (ML) to mine hidden rules in data, conforming
to the" fourth paradigm". A great deal of basic data is needed for this method. By comparing …

Artificial intelligence approach for data-driven design is employed to design an alumina
reinforced aluminium matrix composite (AMC) with improved tribo-mechanical properties …

Computational intelligence based modeling and optimization techniques are employed
primarily to investigate the role of the composition and processing parameters on the …

Several statistical techniques are used for the design of materials through extraction of
knowledge from existing data banks. These approaches are getting more attention with the …

数据驱动方法利用机器学习算法挖掘数据中隐藏的规则, 是一种符合“第四范式” 的研究方法.
该研究方法的开展基于大量材料基础数据. 通过对比国内外材料基础数据平台 …

The properties of steels depend in a complex way on their composition and heat treatment
and neural networks have therefore recently been widely used for capturing these …