Constitutive artificial neural networks: a general anisotropic constitutive modeling framework utilizing machine learning

In this contribution, a novel machine learning architecture for data-driven modeling of the mechanical constitutive behavior of materials, constitutive artificial neural networks (CANNs) [1], will be introduced. CANNs incorporate basic material modeling fundamentals from continuum mechanics while relying on artificial neural networks for material-specific relations. Their architecture allows them to process stress-strain curves and arbitrary additional information (e.g., about the microstructure or manufacturing parameters). With only a low-to-moderate amount of training data and training time, they can predict the constitutive behavior of complex nonlinear and anisotropic materials. The ability to utilize additional material-specific information enables CANNs to predict the mechanical behavior of previously unseen materials if the CANN was sufficiently trained with many similar materials.

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MLE@TUHH

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Constitutive artificial neural networks: a general anisotropic constitutive modeling framework utilizing machine learning