I am supported by Pasteur Labs & Institute of Simulation Intelligence to produce freely available educational resources on advanced mathematical topics on my YouTube channel: https://www.youtube.com/@MachineLearningSimulation

Research on machine learning (in particular deep learning) for physical simulations (like fluid and solid mechanics), adjoint methods and automatic differentiation, inverse problems as well as optimization algorithms.I am supervised by Nils Thuerey, https://ge.in.tum.de/

Machine Learning accelerated Computational Fluid Dynamics by learning Local Correction Models using the Adjoint Method.I was supervised by Dr. Dirk Hartmann (Siemens) and Prof. Thomas Richter (OVGU Magdeburg).

- Usage of Machine Learning to create explicit Model-Predictive Controller Rules out of sampled trajectories- Gained insight into sklearn and gplearn library- Parallelization of Large Machine Learning Tasks using Dask on a Linux cluster

- Simulation of Magnetic Levitation and Propulsion using ANSYS Maxwell- Multi-Physics Simulation Coupling in an Multi-Disciplinary Optimization using openMDAO- Usage of the Leibniz Compute Center (LRZ) for High Performance Computing

As an aid for the lectures and teaching in engineering mathematics I conceptualized and developed a web application for interactive exploration of mathematical ideas. Therefore, I used the Python libraries Bokeh and Flask in combination with an Apache server under a Linux environment.

- Higher Engineering Mathematics (vector and tensor analysis, linear algebra, analytical treatment of ordinary and partial differential equations)- Thermodynamics- Heat Transfer

Software Engineering for the in-house topology optimization suite:- C++ extension of the heuristic based optimization approach by response function sensitivities based on a binary material model- Gained experience in CAE and CAD software: ANSA, Metapost, Hypermesh, Optistruct, Abaqus- Shell and Python Scripting- Usage of the Volkswagen HPC Cluster in Wolfsburg

Mandatory preparation for my studies in mechanical engineering.