FEANN - An efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining. - Karl A. Kalina; Lennart Linden; Jörg Brummund; Markus Kästner (CoRR, 2022)

Neural networks meet hyperelasticity: A guide to enforcing physics. - Lennart Linden; Dominik K. Klein; Karl A. Kalina; Jörg Brummund; Oliver Weeger; Markus Kästner (CoRR, 2023)

A comparative study on different neural network architectures to model inelasticity. - Max Rosenkranz; Karl A. Kalina; Jörg Brummund; Markus Kästner (CoRR, 2023)

Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria. - Karl A. Kalina; Philipp Gebhart; Jörg Brummund; Lennart Linden; WaiChing Sun; Markus Kästner (CoRR, 2023)

Viscoelasticty with physics-augmented neural networks: Model formulation and training methods without prescribed internal variables. - Max Rosenkranz; Karl A. Kalina; Jörg Brummund; WaiChing Sun; Markus Kästner (CoRR, 2024)

Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functions. - Karl A. Kalina; Jörg Brummund; WaiChing Sun; Markus Kästner (CoRR, 2024)

A dual-stage constitutive modeling framework based on finite strain data-driven identification and physics-augmented neural networks. - Lennart Linden; Karl A. Kalina; Jörg Brummund; Brain Riemer; Markus Kästner (CoRR, 2025)