8 citations · 14 across the 4 of their papers we have counts for
4 papers
Physics-based Machine Learning for Computational Fracture Mechanics
Fadi Aldakheel, Elsayed S. Elsayed, Yousef Heider +1
This study introduces a physics-based machine learning framework for modeling both brittle and ductile fractures. Unlike physics-informed neural networks, which solve partial diffe…
TwinLab: a framework for data-efficient training of non-intrusive reduced-order models for digital twins
Maximilian Kannapinn, Michael Schäfer, Oliver Weeger
Purpose: Simulation-based digital twins represent an effort to provide high-accuracy real-time insights into operational physical processes. However, the computation time of many m…
Polyconvex neural network models of thermoelasticity
Jan N. Fuhg, Asghar Jadoon, Oliver Weeger +2
Machine-learning function representations such as neural networks have proven to be excellent constructs for constitutive modeling due to their flexibility to represent highly nonl…
Nonlinear electro-elastic finite element analysis with neural network constitutive models
Dominik K. Klein, Rogelio Ortigosa, Jesús Martínez-Frutos +1
In the present work, the applicability of physics-augmented neural network (PANN) constitutive models for complex electro-elastic finite element analysis is demonstrated. For the i…