  Machine learning models are increasingly used in many engineering fields
thanks to the widespread digital data, growing computing power, and advanced
algorithms. Artificial neural networks (ANN) is the most popular machine
learning model in recent years. Although many ANN models have been used in the
design and analysis of composite materials and structures, there are still some
unsolved issues that hinder the acceptance of ANN models in the practical
design and analysis of composite materials and structures. Moreover, the
emerging machine learning techniques are posting new opportunities and
challenges in the data-based design paradigm. This paper aims to give a
state-of-the-art literature review of ANN models in the nonlinear constitutive
modeling, multiscale surrogate modeling, and design optimization of composite
materials and structures. This review has been designed to focus on the
discussion of the general frameworks and benefits of ANN models to the above
problems. Moreover, challenges and opportunities in each key problem are
identified and discussed. This paper is expected to open the discussion of
future research scope and new directions to enable efficient, robust, and
accurate data-driven design and analysis of composite materials and structures.
