Hillgärtner, MarkusMarkusHillgärtnerLinka, KevinKevinLinkaAbdolazizi, Kian PhilippKian PhilippAbdolaziziAydin, Roland C.Roland C.AydinItskov, MikhailMikhailItskovCyron, Christian J.Christian J.Cyron2022-01-262022-01-262021-1292nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM 2021)http://hdl.handle.net/11420/11563In 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.en1617-706120211MLE@TUHHConference Paper10.1002/pamm.202100072Conference Paper