Physics-based Digital Twins for Autonomous Thermal Food Processing: Efficient, Non-intrusive Reduced-order Modeling. - Maximilian Kannapinn; Minh Khang Pham; Michael Schäfer 004 (CoRR, 2022)

Autonomous Cooking with Digital Twin Methodology. - Maximilian Kannapinn; Michael Schäfer 004 (CoRR, 2022)

Endeavouring Intelligent Process Self-Control by Employing Digital Twin Methodology: Proof-of-Concept Study for Cooking Applications. - Maximilian Kannapinn; Michael Schäfer 004 (CoRR, 2022)

TwinLab: a framework for data-efficient training of non-intrusive reduced-order models for digital twins. - Maximilian Kannapinn; Michael Schäfer 004; Oliver Weeger (CoRR, 2024)

Digital twin inference from multi-physical simulation data of DED additive manufacturing processes with neural ODEs. - Maximilian Kannapinn; Fabian J. Roth; Oliver Weeger (CoRR, 2024)

Neural networks meet hyperelasticity: A monotonic approach. - Dominik K. Klein; Mokarram Hossain; Konstantin Kikinov; Maximilian Kannapinn; Stephan Rudykh; Antonio J. Gil (CoRR, 2025)

Stable Port-Hamiltonian Neural Networks. - Fabian J. Roth; Dominik K. Klein; Maximilian Kannapinn; Jan Peters 001; Oliver Weeger (CoRR, 2025)