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This book introduces the approach of Machine Learning (ML) based
predictive models in the design of composite materials to achieve
the required properties for certain applications. ML can learn from
existing experimental data obtained from very limited number of
experiments and subsequently can be trained to find solutions of
the complex non-linear, multi-dimensional functional relationships
without any prior assumptions about their nature. In this case the
ML models can learn from existing experimental data obtained from
(1) composite design based on various properties of the matrix
material and fillers/reinforcements (2) material processing during
fabrication (3) property relationships. Modelling of these
relationships using ML methods significantly reduce the
experimental work involved in designing new composites, and
therefore offer a new avenue for material design and properties.
The book caters to students, academics and researchers who are
interested in the field of material composite modelling and design.
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