Constitutive artificial neural networks: a general anisotropic constitutive modeling framework utilizing machine learning. Issue 1 (14th December 2021)

• Record Type: Journal Article
• Authors: Hillgärtner, Markus; Linka, Kevin; Abdolazizi, Kian P.; Aydin, Roland C.; Itskov, Mikhail; Cyron, Christian J.
• Editors: Kaliske, M.
• Is Part Of: Proceedings in applied mathematics and mechanics. Volume 21:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-14
• Subjects: Applied mathematics -- Periodicals; Engineering mathematics -- Periodicals; Mathematical physics -- Periodicals; 519
• DOI: 10.1002/pamm.202100072
• Languages: English
• ISSNs: 1617-7061

Abstract

Abstract: 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.