Alexander Karl Rothkopf

Professor i materialfysikk

Alexander Karl Rothkopf
51832021
KE-E 537
Det teknisk-naturvitenskapelige fakultet
Institutt for matematikk og fysikk
KE E-537
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Bio

Research Profile:

I work on elucidating the real-time properties of strongly coupled quantum systems, such as e.g. the quark-gluon plasma created in relativistic heavy-ion collisions. To this end I study the spectral properties of bound states and single particles in QCD and scalar theories. The non-perturbative nature of the problems at hand leads me to use predominantly numerical methods, such as lattice QCD. A particular interest lies in the extraction of spectral functions from Euclidean correlator data using Bayesian inference.

Selected ongoing research projects:

1) Determination of the heavy quark potential from realistic lattice QCD simulations with novel machine-learning assisted spectral extraction methods ( with Gaurang Parkar (UiS), Rasmus N. Larsen (UiS), Peter Petreczky (BNL), Swagato Mukherjee (BNL))

2) Elucidating the real-time dynamics of heavy quarkonium states based on the Open-Quantum-Systems framework ( with Y. Akamatsu (U. Osaka) )

3) Development of improved discretization schemes for the accurate simulation of Open-Quantum-Systems dynamics ( with J. Nordström (U. Linköping) )

4) Development of novel machine-learning assisted real-time simulation strategies for strongly correlated quantum fields ( with D. Alvestad (UiS) )

Teaching:

1) Introduction to Quantum Mechanics

This course provides the students with a first introduction to the field of quantum mechanics. It both allows students to get familiar with the novel concepts of quantum mechanics and to train the computational skills needed to solve simple problems.

2) Quantum particles and fields on the lattice 

This course provides a theoretical and practical introduction to the description of quantum particles and fields in a discretized setting, relevant for e.g. numerical simulations of the strong interactions (lattice QCD). Besides regular lectures the course features an hands-on tutorial in which students themselves implement simulation strategies.

 

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