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: FIS_scads Fiber modeling, area_architectures plastics, reinforced Constitutive Machine identification learning, networks, Parameter topic_engineering Neural]
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: Convolutional FIS_scads Fiber area_architectures neural plastics rate dependency, reinforced Direct Strain Machine learning, networks, identification, parameter topic_engineering] 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: FIS_scads User Nystagmus Determination, Parameter Classification, Modelling Eye-Movement area_responsibleai]