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Stefan Hildebrand is a research assistant at the Department of Structural and Computational Mechanics (SMB, chair of Prof. Dr.-Ing. habil. Sandra Klinge) at TU Berlin since October 2021, with guest research stays at Georgia Tech (chair of Professor Surya Kalidindi, Ph.D.) and Indian Institute of Technology Bombay (chair of Professor Krishnendu Haldar, Ph.D.). His main research topics are Machine Learning methods in solid body mechanics, with a focus on constitutive models including damage and cyclic plasticity, Neural FEM as well as vibration problems. A key focus of his work is the coupling of the different phenomena in unified Machine Learning architectures. Besides, he conducts research in the field of computer science and STEM education.

Stefan Hildebrand received his Master’s degree in Computational Engineering Sciences from Technische Universität Berlin in April 2021 as the best of his year. The topic of his thesis was the performant high fidelity simulation of radial journal bearings for the application in multi body systems.

From 2016 through September 2021, he worked at CONTECS engineering services GmbH as software development engineer, IT security and data protection officer as well as IT system administrator. Before, he was student assistant at TU Berlin for computer aided design since 2013.

Publications:

1. Stefan Hildebrand, Jonathan Georg Friedrich, Melika Mohammadkhah, Sandra Klinge.

Coupled CANN-DEM simulation in solid mechanics. Mach. Learn.: Sci. Technol. 6 015038,

2025.

2. Stefan Hildebrand, Sandra Klinge. 

Hybrid data-driven and physics-informed regularized learning of cyclic plasticity with neural networks. Mach. Learn.: Sci. Technol. 5 045058, 2024.

3. Stefan Hildebrand, Sandra Klinge. 

Comparison of neural FEM and neural operator methods for applications in solid mechanics. Neural Comput & Applic 36, 16657–16682, 2024.

4. Simon Gallinger, Natalie Jankowski, Milena Bister, Sandra Korge, Astrid Trachterna, Stefan Hildebrand, Tamer Oruc, Jörg Niewöhner, Urte Heitman, Robert Downes and Marc Kraft. Development of a modular Decubitus Prophylaxis System: DekuProSys. Curr. Dir. Biomed. Eng., 5 (1) : 277–280, 2019.

Conferences:

1. K.H. Chai, S. Hildebrand, T. Lachnit, M. Benfer, G. Lanza, S. Klinge.

“Accelerating Fleet Upgrade Decisions with Machine-Learning Enhanced Optimization”. Procedia CIRP 139 (2026): 7-12.

2. S. Hildebrand, S. Klinge. 

“Hybrid data-driven and physics-informed learning of cyclic plasticity for pipe steel”. Proceedings of the 95th Annual Meeting of GAMM (2026).

3. S. Hildebrand, S. Klinge. 

“Physics-Informed Neural Network modeling of Cyclic Plasticity for steel alloy 4130”. Procedia Structural Integrity 72 (2025): 520-528.

4. S. Hildebrand, S. Klinge. 

“Vibrations analysis of structural properties with uncertainties using oscillatory Physics-Informed Neural Networks (oPINN)”. UNCECOMP 2025 6th ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering (2025).

5. C.S. Arlinghaus, S. Hildebrand, C. Neumann. 

“BYTE Challenge - from competition to STEM platform”. WiPSCE '22, Proceedings of the 17th Workshop in Primary and Secondary Computing Education, 2022.

6. D. Göhlich, S. Hildebrand and D. D. Schellert. “Augmented DSM Sequencing to Support Product Development Planning”. DS 92: Proceedings of the DESIGN 2018 15th International Design Conference, 2018.

Awards and commitments:

• Speaker of the Junior-Fellows of the German Computer Science Association (GI)

• Member of the board of the German Computer Science Association (GI)

• Member of the Society for Applied Mathematics and Mechanics (GAMM)

• Forbes 30U30 (DACH), 2023

• Audience Choice Award at Euromech Colloquium 650 at University of Belgrade

Current Affiliation:

Department of Structural and Computational Mechanics, Technische Universität Berlin