About me

I am a post-doctoral researcher in the group of Caroline Lasser. Before joining TUM I completed my PhD with Christian Lubich at the University of Tübingen.

My research focuses on numerical analysis and its applications in quantum physics. I work on mesh-free methods for quantum dynamics, in particular Gaussian wave packets, as well as on tensor-based methods in the context of dynamical low-rank approximations.

During my PhD, I concentrated on dynamical low-rank approximation. I developed and analyzed time integration schemes for tree tensor networks, a data-sparse hierarchical tensor decomposition. The key idea is to evolve each low-rank factor in time to preserve the decomposed structure throughout the dynamics. This led to the development of a class of Basis Update and Galerkin (BUG) integrators for tree tensor networks, enabling robust and efficient time integration. My work also included the construction of Hamiltonians in tree tensor network format and applications to (open) long-range interacting quantum spin systems.

Publications and preprints

• G. Ceruti, D. Kressner, and D. Sulz. Low-rank tree tensor network operators for long-range pairwise interactions. SIAM Journal on Scientific Computing, 47(4):A2248–A2271, 2025.
• D. Sulz. Time Integration in Quantum Dynamics using Tree Tensor Networks, PhD Thesis, University of Tübingen, Germany, 2024.
• G. Ceruti, J. Kusch, C. Lubich, and D. Sulz. A parallel basis update and galerkin integrator for tree tensor networks. arXiv preprint arXiv:2412.00858, 2024.
• L. Schmid, D. Sulz, and L. Schmalen. Fast and robust expectation propagation mimo detection via preconditioned conjugated gradient. In 2024 58th Asilomar Conference on Signals, Systems, and Computers, pages 1603–1608. IEEE, 2024.
• D. Sulz, C. Lubich, G. Ceruti, I. Lesanovsky, and F. Carollo. Numerical simulation of long-range open quantum many-body dynamics with tree tensor networks. Physical Review A, 109(2):022420, 2024.
• G. Ceruti, C. Lubich, and D. Sulz. Rank-adaptive time integration of tree tensor networks. SIAM J. Numer. Anal., 61(1):194–222, 2023.

A list of previous courses can be found on TUMonline.