It is important to develop sustainable processes in materials science and manufacturing that
are environmentally friendly. AI can play a significant role in decision support here as …

Experimental results are often plotted as 2-dimensional graphical plots (aka graphs) in
scientific domains depicting dependent versus independent variables to aid visual analysis …

This paper describes the development and use of MatML, the Materials Markup Language.
MatML is an emerging XML standard intended primarily for the exchange of materials …

Scientific data is often analyzed in the context of domain-specific problems, for example,
failure diagnostics, predictive analysis, and computational estimation. These problems can …

Scientific datasets often consist of complex data types such as images. Mining such data
presents interesting issues related to semantics. In this paper, we explore the research …

Experimental results in scientific domains are often plotted as graphs of process variables.
Clustering such graphs is useful for applications such as process comparison in which …

In scientific domains, knowledge is often discovered from experiments by grouping or
clustering them based on the similarity of their output. The causes of similarity are analyzed …

This paper describes a graphical data mining system called AutoDomainMine. It is based on
our proposed approach of integrating clustering and classification to mine scientific data …

In domains such as Materials Science experimental results are often plotted as two-
dimensional graphs of a dependent versus an independent variable to aid visual analysis …

In this paper we propose a novel approach to accelerate the multi-scale simulation of metal
plasticity. In macroscopic zones of nearly homogeneous strain responses, the evolution of …