A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP. Computational Materials Science, (244 (2024))Elsevier Science B.V., September 2024. [PUMA: area_architectures topic_engineering Constitutive FIS_scads Fiber Machine Neural Parameter identification learning, modeling, networks, plastics, reinforced]
Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning. ECCM21 - Proceedings of the 21st European Conference on Composite Materials, (3):1252--1259, European Society for Composite Materials (ESCM), Jul 2, 2024. [PUMA: area_architectures topic_engineering Convolutional Direct FIS_scads Fiber Machine Strain dependency, identification, learning, networks, neural parameter plastics rate reinforced] URL
Automated Nystagmus Parameter Determination: Differentiating Nystagmic from Voluntary Eye-Movements. In Vincent G. Duffy (Eds.), Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management - 14th International Conference, DHM 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Proceedings, 341--354, Springer, Berlin u. a., Germany, 2023. [PUMA: area_responsibleai Classification, Determination, Eye-Movement FIS_scads Modelling Nystagmus Parameter User]