Zofia Trstanova


G. Stoltz, Z. Trstanova, Langevin dynamics with general kinetic energies, accepted in SIAM MMS, 2018.

Z. Trstanova, S. Redon, Estimating the speed-up of Adaptively Restrained Langevin Dynamics, J. Comp. Phys., 336:412-428, 2017.

S. Redon, G. Stoltz, Z. Trstanova. Error analysis of modified Langevin dynamics. J. Stat. Phys., 164 (4) :735-771, 2016.

Z. Trstanova, Mathematical and Algorithmic Analysis of Modified Langevin Dynamics, 2016.

In review

R. Banisch, Z. Trstanova, A. Bittracher, S. Klus, P. Koltai, Diffusion maps tailored to arbitrary non-degenerate Ito processes , arXiv:1710.03484, 2017.


For the most recent updates on my work, see my github page.


This is a newely developped python package for diffusion maps. You can now simply install it using "pip install pydiffmap" or directly from https://github.com/DiffusionMapsAcademics/pyDiffMap. See the documentation for jupyter notebooks with some nice examples.

Thermodynamic Analytics ToolkIt

This software is developed in collaboration with Alan Turing Institute for Data science. The program is based on TensorFlow and it allows to train neural networks using thermodynamics.


The Molecular Integration Simulation Tooklit (MIST) is being developed as part of the ExTASY (Extensible Tools for Advanced Sampling and analYsis) project. In order for new integration algorithms to be widely adopted by the biomolecular simulation community, their effectiveness must be demonstrated on systems of biological relevance. Typically, integrator development is carried out within simplified MD codes, which lack the efficient force evaluation and paralellisation approaches available in 'production' MD codes such as GROMACS, NAMD and LAMMPS, which have large existing user bases. The excellent performance and scalability of these codes comes at a cost of software complexity, and thus there is a barrier to the implementation of new algorithms, their testing at scale, and their eventual adoption by the wider community.