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
Inverse Dirichlet weighting enables reliable training of physics informed neural networks. Machine learning: science and technology, (3)1IOP Publishing Ltd., Feb 15, 2022. [PUMA: topic_lifescience ALGORITHM FIS_scads active catastrophic flow forgetting, gradient modeling, multi-objective multi-scale networks, neural physics-informed regularization, training, turbulence,]
Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems. Journal of computational physics, (491)Academic Press Inc., Oct 15, 2023. [PUMA: topic_lifescience Adaptive Artificial Bayesian Carlo, FIS_scads Hamiltonian Intelligence, Monte Multi-objective Quantification Uncertainty learning, networks, neural physics-informed training, weight]
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,]
Learning computable models from data. 1--6, 2021. [PUMA: Differential ENO-WENO, FIS_scads Neural Surrogate modeling networks, operators,]