Christopher Straub

I am a postdoctoral researcher at the AI-augmented Simulation group at Fraunhofer Institute for Integrated Systems and Device Technology IISB in Erlangen (Germany). My research focuses on scientific machine learning, in particular,

physics-informed machine learning and its industrial applications,
methodological improvements of physics-informed neural networks.

I have a background in mathematics, where I obtained my PhD in March 2024 under the joint supervision of Gerhard Rein (University of Bayreuth) and Mahir Hadžić (University College London). My doctoral research was focused on the analysis of partial differential equations from galactic dynamics using mathematical and numerical methods. Concretely, I worked on

proving the existence of damped and pulsating galaxies,
studying the stability of star clusters in general relativity,
simulating non-linear PDE systems numerically via high-performance computing in C++.

Recent news:

Aug 19, 2025	My paper A Birman–Schwinger Principle in General Relativity: Linearly Stable Shells of Collisionless Matter Surrounding a Black Hole with S. Günther and G. Rein has been published.
Jul 17, 2025	My paper Damping Versus Oscillations for a Gravitational Vlasov-Poisson System with M. Hadžić, G. Rein, and M. Schrecker has been published.
Jul 12, 2025	I gave a keynote talk Mit Mathematik zu pulsierenden Galaxien und verbesserten Mikrochips at the 17th Mathematics Day at the University of Bayreuth. [News article] [LinkedIn post]
Mar 24, 2025	I will be attending some conferences this spring, including the meeting of EMS activity group on Scientific Machine Learning and the Annual GAMM meeting. Looking forward to discussing physics-informed machine learning with you there!
Mar 06, 2025	My paper Hard-constraining Neumann boundary conditions in physics-informed neural networks via Fourier feature embeddings has been accepted at the ICLR 2025 Workshop on Machine Learning Multiscale Processes. [LinkedIn post]