Georg Trede
Georg sitting on a fallen tree during a hike in the forest

Physics · Machine learning · Dynamical systems

Hi, I’m Georg.

I’m a physics PhD student at Heidelberg University, working on how machine learning can help us understand and predict dynamical systems.

Research, projects & a little beyond.Scroll to explore

Research

Dynamical systems.
Scientific machine learning.

A Lorenz system moving between periodic and chaotic motion.
Lorenz dynamics: periodic to chaotic and back. Manual control pauses automatic changes for 30 seconds.

My research sits at the intersection of computational physics, machine learning and dynamical systems.

I work on reconstructing dynamical systems from time series and predicting their behaviour beyond the conditions seen during training. A particular focus is on non-autonomous systems, where the conditions under which they evolve change over time. I’m also interested in how models can generalize to new dynamical regimes, including across tipping points.

My approach combines mathematical analysis with data-driven modelling: understanding where existing models reach their limits, and developing methods that can go beyond them.

Research group

Department of Theoretical Neuroscience

Central Institute of Mental Health, Mannheim
Heidelberg University

Meet the lab

Publications & academic work

Publications & academic work.

Scientific machine learning
PreprintJune 2026arXiv:2606.22969

Topological Out-of-Domain Generalization in Dynamical Systems Reconstruction

Georg Trede, Charlotte Ricarda Doll, Elias Weber & Daniel Durstewitz

How well do learned models predict a system’s behaviour beyond their training conditions? We analyse structural limitations of existing reconstruction models and show how feature splitting enables predictions in unseen dynamical regimes, including across tipping points, without retraining.

Equal contribution with Charlotte Ricarda Doll.

Master’s thesis2024Physics · Heidelberg University

Neural Flow Operators for Solving Advection-Diffusion Systems

Georg Trede

My master’s thesis explores how Fourier Neural Operators can accelerate simulations of turbulent atmospheric flows. Trained on Large Eddy Simulation data using curriculum learning and generalised teacher forcing, the models reproduce key features of atmospheric turbulence. Training on full three-dimensional data improves accuracy, with an estimated speedup of up to 65× in the evaluated setup. Adding local differential kernels further improves accuracy, but reduces the speedup.

The thesis is available upon request.

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Bachelor’s thesis2022Physics · University of Münster & Australian National University Canberra

Auto Alignment Toolkit for Optical Resonators

Georg Trede

Developed in collaboration with the Australian National University (ANU) in Canberra, my bachelor’s thesis explores how machine learning can reduce the time spent aligning lasers. It combines image-based mode classification, neural-network optimisation and computer-controlled actuators to align laser beams to optical resonators. The toolkit achieved mode-matching efficiencies of up to 88.4% of the physically achievable limit and reduced the need for human intervention, although resonator locking still required manual calibration.

The thesis is available upon request.

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Beyond research

Scouting, sport & time outdoors.

I’m passionate about scouting, and I play and train unicycle hockey through Heidelberg University’s Hochschulsport.

Apart from that, I love spending time outdoors, reading a good book, or building a small website for fun (see projects below).

Explore my projects

Get in touch

Let’s connect.

Questions about my research, shared interests or just saying hello — I’d be happy to hear from you.