Research Projects

Machine learning phases of matter

Using machine learning techniques to detect phase transitions autonomously from readily accessible data.

JA in collaboration with Frank Schäfer, Niels Lörch, Flemming Holtorf, Christoph Bruder, Alan Edelman, Martin Žonda, and Axel Lode

Machine learning phases of matter

Differentiable isospectral flows for matrix diagonalization

Implementing continuous isospectral flows in a differentiable manner to compute derivatives efficiently and accurately, enabling the solution of inverse problems.

JA in collaboration with Frank Schäfer, Heinz Krummenacher, Stefan Kehrein, and Christoph Bruder

Differentiable isospectral flows for matrix diagonalization

Performance bounds for quantum control

Bounding the best attainable performance for quantum control problems using moment-sum-of-squares techniques.

JA in collaboration with Flemming Holtorf, Frank Schäfer, Christopher Rackauckas, and Alan Edelman

Performance bounds for quantum control

Machine learning molecular collisions

Modelling changes in energy and state population distributions of molecular systems undergoing collisions using machine learning methods.

JA in collaboration with Debasish Koner, Juan Carlos San Vicente Veliz, Narendra Singh, Raymond Bemish, Silvan Käser, and Markus Meuwly

Machine learning molecular collisions

Entropy production in ticking clocks

Studying the relation between the entropy production per tick of ticking clocks and their accuracy.

JA in collaboration with Mischa Woods, Christoph Bruder, and Renato Renner

Entropy production in ticking clocks