17 citations · 25 across the 3 of their papers we have counts for
4 papers
A mesh-based neural energy method for the simulation of heterogeneous composites
Pius J. M. Wichmann, Stefan Hildebrand, Sandra Klinge
Modeling heterogeneous materials remains a challenge for physics-informed neural networks such as the deep energy method (DEM). The DEM and its variants, here collectively referred…
Accelerating Fleet Upgrade Decisions with Machine-Learning Enhanced Optimization
Kenrick Howin Chai, Stefan Hildebrand, Tobias Lachnit +3
Rental-based business models and increasing sustainability requirements intensify the need for efficient strategies to manage large machine and vehicle fleet renewal and upgrades.…
Hybrid data-driven and physics-informed regularized learning of cyclic plasticity with Neural Networks
Stefan Hildebrand, Sandra Klinge
An extendable, efficient and explainable Machine Learning approach is proposed to represent cyclic plasticity and replace conventional material models based on the Radial Return Ma…
Comparison of Neural FEM and Neural Operator Methods for applications in Solid Mechanics
Stefan Hildebrand, Sandra Klinge
Machine Learning methods belong to the group of most up-to-date approaches for solving partial differential equations. The current work investigates two classes, Neural FEM and Neu…