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
Spatial–temporal recurrent reinforcement learning for autonomous ships. Neural Networks, (2023)165:634--653, Elsevier Science B.V., Jun 15, 2023. [PUMA: topic_engineering Algorithms, Autonomous COLREG, Computer, Deep FIS_scads Networks, Neural Psychology, Recurrency, Reinforcement, Reward Ships, learning, reinforcement surface vehicle,]