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
Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization. Proceedings of the Genetic and Evolutionary Computation Conference, 1007 -- 1016, Association for Computing Machinery (ACM), United States of America, Jul 14, 2024. [PUMA: topic_engineering FIS_scads imported]
Dancing to the State of the Art?: How Candidate Lists Influence LKH for Solving the Traveling Salesperson Problem. In Michael Affenzeller, Stephan M. Winkler, Anna V. Kononova, Thomas Bäck, Heike Trautmann, Tea Tusar, and Penousal Machado (Eds.), Parallel Problem Solving from Nature – PPSN XVIII, 100--115, Springer, Berlin u. a., Sep 7, 2024. [PUMA: topic_engineering Algorithm Benchmarking, Configuration, FIS_scads Hardness, Heuristic Problem Salesperson Search, Traveling]
Reinvestigating the R2 Indicator: Achieving Pareto Compliance by Integration. In Michael Affenzeller, Stephan M. Winkler, Anna V. Kononova, Thomas Bäck, Heike Trautmann, Tea Tusar, and Penousal Machado (Eds.), Parallel Problem Solving from Nature – PPSN XVIII, 202--216, Springer, Berlin u. a., Sep 7, 2024. [PUMA: topic_engineering Benchmarking, FIS_scads Multi-objective Pareto Performance R2 Utility assessment, compliance, functions indicator, optimization,]
2-Level Reinforcement Learning for Ships on Inland Waterways: Path Planning and Following. Jul 25, 2023. [PUMA: topic_engineering FIS_scads cs.AI, cs.SY eess.SY,]
Two-step dynamic obstacle avoidance. Knowledge-based systems, (302)Elsevier Science B.V., Oct 25, 2024. [PUMA: topic_engineering Deep Dynamic FIS_scads Local Supervised avoidance, learning learning, obstacle path planning, reinforcement]
Artificial neural network small-sample-bias-corrections of the AR(1) parameter close to unit root. Statistica Neerlandica, Wiley-Blackwell, Oxford u. a., Jul 31, 2024. [PUMA: topic_engineering FIS_scads bias correction, network, neural sample small]
Penalized estimation of hierarchical Archimedean copula. Journal of Multivariate Analysis, (201)Academic Press Inc., 2023. [PUMA: topic_engineering Archimedean FIS_scads Hierarchical Maximum Stage-wise copula, estimation estimation, likelihood]
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,]