Results 281 to 290 of about 18,341 (302)
Some of the next articles are maybe not open access.
Outlier-resistant physics-informed neural network
Physical Review ERecent advances in machine learning have introduced physics-informed neural networks (PINN) as a valuable tool for addressing dynamics through governing equations and experimental observations. Outliers can be present in measurements and significantly affect the accuracy of the solutions provided by PINN.
D. H. G. Duarte +2 more
openaire +2 more sources
Physics informed neural networks for continuum micromechanics
Computer Methods in Applied Mechanics and Engineering, 2022Alexander Henkes +2 more
exaly
Physics-informed neural networks for the shallow-water equations on the sphere
Journal of Computational Physics, 2022Alexander Bihlo, Roman O Popovych
exaly
Self-adaptive physics-informed neural networks
Journal of Computational Physics, 2023Ulisses M Braga-Neto
exaly
Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems
Computer Methods in Applied Mechanics and Engineering, 2022Kevin Linka +2 more
exaly
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
Journal of Computational Physics, 2020Ameya D Jagtap +2 more
exaly
Physics informed neural networks for control oriented thermal modeling of buildings
Applied Energy, 2022Gargya Gokhale +2 more
exaly