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Over the past few years, a few experimental failure data of composites have been collected. It would be of interest to leverage the existing data to improve the prediction of failure criteria. In this paper, we developed a framework that combines sparse regression with compressed sensing to discover failure criteria of composites from experimental data, which leveraging advances in sparsity techniques and machine learning. This framework does not need Bigdata to train the model and is remarkably robust to the noised data, which satisfies the constraints of the current failure data. To test the performance of the proposed method, we collected the experimental data from the first Worldwide Failure Exercise (WWFE I) and fed it to the proposed method. To satisfy the engineering design needs, we proposed an optimization approach to enforce a constraint to the discovered failure criterion to yield a conservative model. Three examples were presented to demonstrate the proposed framework. The result shows that the proposed framework can identify the most important features and the discovered failure criterion match the experiment result well. Besides, with the enforced constraint, the proposed method can yield a conservative failure criterion. [ABSTRACT FROM AUTHOR]

Machine learning models are increasingly used in many engineering fields thanks to the widespread digital data, growing computing power, and advanced algorithms. The most popular machine learning model in recent years is artificial neural networks (ANN). Although many ANN models are used in the constitutive modeling of composite materials, 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 posing 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 constitutive modeling of composite materials, focusing on discovering unknown constitutive laws and accelerating multiscale modeling. This review focuses on the general frameworks, benefits, and challenges and opportunities of ANN models to the constitutive modeling of composite materials. Moreover, potential applications of ANN-based constitutive models in composite materials and structures are also discussed. This review is intended to initiate discussion of future research scope and new directions to enable efficient, robust, and accurate data-driven design and analysis of composite materials and structures. • Applications of ANN models in composites constitutive modeling are reviewed. • Challenges of discovering constitutive laws are discussed. • Challenges of accelerating multiscale modeling are summarized. • Potential solutions to the challenges of ANN constitutive models are proposed. • Future opportunities for ANN-based constitutive models are discussed. [ABSTRACT FROM AUTHOR]