Polynomial Neural Forms Using Feedforward Neural Networks for Solving Differential Equations. - Toni Schneidereit; Michael Breuß (ICAISC (1), 2021)

Collocation Polynomial Neural Forms and Domain Fragmentation for Initial Value Problems. - Toni Schneidereit; Michael Breuß (CoRR, 2021)

Adaptive Neural Domain Refinement for Solving Time-Dependent Differential Equations. - Toni Schneidereit; Michael Breuß (CoRR, 2021)

Collocation polynomial neural forms and domain fragmentation for solving initial value problems. - Toni Schneidereit; Michael Breuß (Neural Comput. Appl., 2022)

Computational characteristics of feedforward neural networks for solving a stiff differential equation. - Toni Schneidereit; Michael Breuß (Neural Comput. Appl., 2022)

YOLO-Based Object Detection in Industry 4.0 Fischertechnik Model Environment. - Slavomira Schneidereit; Ashkan Mansouri Yarahmadi; Toni Schneidereit; Michael Breuß; Marc Gebauer (IntelliSys (2), 2023)

The solution of time-dependent ordinary differential equations using neural networks: collocation polynomial neural forms and adaptive neural domain refinement. - Toni Schneidereit (BTU Cottbus - Senftenberg, 2023)

Object detection characteristics in a learning factory environment using YOLOv8. - Toni Schneidereit; Stefan Gohrenz; Michael Breuß (CoRR, 2025)

Strategies for training point distributions in physics-informed neural networks. - Santosh Humagain; Toni Schneidereit (CoRR, 2025)