Jonas Kneifl

Jonas Kneifl

Scientific Machine Learning researcher | PhD

University of Stuttgart, Institute of Engineering and Computational Mechanics

Biography

Greetings! I recently completed my PhD, focusing on scientific machine learning. My academic journey led me to the captivating world of engineering cybernetics, where I honed my skills in mathematics, mechanics, and control theory, and formed a deep interest in systems theory and understanding complex models.

In my research, I’m driven to efficiently approximate complex models by blending core numerical methods with artificial intelligence learning techniques. I’m passionate about the synergy between scientific principles and cutting-edge technologies.

Interests
  • Scientific Machine Learning
  • Model Order Reduction
  • Surrogate Modeling
Education
  • PhD in the field of Scientific Machine Learning, 2025

    University of Stuttgart

  • MSc in Engineering Cybernetics, 2020

    University of Stuttgart

  • BSc in Engineering Cybernetics, 2017

    University of Stuttgart

Experience

 
 
 
 
 
Institute of Engineering and Computational Mechanics
Research Associate
June 2020 – October 2025 University of Stuttgart, Stuttgart, Germany
Researcher in the field of Scientific Machine Learning within the cluster of excellence “Data-Integrated Simulation Science (SimTech)” and lecture assistant.
 
 
 
 
 
Department of Civil and Environmental Engineering
Visiting Researcher
September 2023 – September 2023 Polytechnic University of Milan, Milan, Italy
Development of a reduced-order modeling with uncertainty quantification framework using generative AI algorithms.
 
 
 
 
 
Artificial Intelligence Institute in Dynamic Systems
Research Intern
August 2022 – November 2022 University of Washington, Seattle (US)
Development of a multi-hierarchic surrogate modeling approach using graph convolutional neural networks and mesh simplification.
 
 
 
 
 
Robert Bosch GmbH
Working Student
May 2019 – April 2020 Stuttgart, Germany
Process automation in project planning within the area of autonomous driving.
 
 
 
 
 
Daimler AG
Industrial Internship
September 2018 – April 2019 Sindelfingen, Germany
Scenario generation and tool development for the simulative validation of autonomous driving systems.

Publications

(2025). Data-driven identification of latent port-Hamiltonian systems. Comput. Sci. Eng..

Cite Code

(2024). Multi-hierarchical surrogate learning for explicit structural dynamical systems using graph convolutional neural networks. Computational Mechanics.

Cite DOI

(2024). On using machine learning algorithms for motorcycle collision detection. Springer Science and Business Media LLC.

Cite DOI

(2024). VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantification. arXiv.

Cite Code DOI arXiv

(2023). Low-dimensional data-based surrogate model of a continuum-mechanical musculoskeletal system based on non-intrusive model order reduction. Archive of Applied Mechanics.

Cite DOI URL

Contact