SUSTAINABLE
SYSTEMS
SCIENCE

We combine methods from mathematics, systems theory, data science, and scientific computing with the aim to rethink data-intensive systems research. Based on high-quality research data, we develop digital solutions that are used across disciplines.

Lately at CASUS

CASUS research team leader appointed full professor at TU Dresden

Prof. Justin Calabrese now 2nd full professor working at CASUS

Atomistic simulation

The simulation software CP2K enables AI models. A new usage guide now introduces the software suite to a broad audience.

A prominent addition to the cityscape

Interim locations of CASUS will be consolidated in historic building complex between Lunitz and Grüner Graben
The Center for Advanced System Understanding is moving into a building on Lunitzbach. The new location will increase the center’s appeal to qualified personnel and raise its profile in the city.
VIEW MORE

Do changing vegetation patterns reveal the risk of desertification?

CASUS project successful in Germany-wide tender

qFLOW aims to enable better simulations of complex flow processes – a boon for environmental, water, and climate research.
VIEW MORE

Biomedical engineering project receives funding

ProtheraEGFR aims to tackle cancer resistance with artificial intelligence-designed miniproteins
Three Helmholtz centers, among them the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) with its Görlitz-based institute CASUS, and one University Medical Center will receive 1.5 million euro.
VIEW MORE

Let's talk about it

Water-driven materials: a multi-scale systems approach from molecule to device

14 January 2026

CASUS Distinguished Lecture

The talk introduces a new class of sustainable, interactive materials whose functionality arises from the interplay between hierarchical structures of hard matter and water.
VIEW MORE

Damar Wicaksono, Uwe Hernandez Acosta, Sachin Krishnan Thekke Veettil, Jannik Kissinger and Michael Hecht - Journal of Open Source Software 10 (109), 7702 (2025)

Interpolation is essential in various computational tasks, including function approximation, curve fitting, numerical integration, differential geometry, spectral methods, optimization, and uncertainty quantification. Minterpy is an open-source Python package designed for multivariate polynomial interpolation. It provides stable and …

Gabriel della Maggiora, Luis Alberto Croquevielle, Harry Horsley, Thomas Heinis, Artur Yakimovich - Proceedings of the AAAI Conference on Artificial Intelligence 39 (3), 2672-2680 (2025)

Phase imaging is gaining importance due to its applications in fields like biomedical imaging and material characterization. In biomedical applications, it can provide quantitative information missing in label-free microscopy modalities. One of the most prominent methods in phase quantification is the Transport-of-Intensity Equation (TIE). TIE often requires multiple acquisitions at different defocus distances, …

Tobias Dornheim, Tilo Döppner, Panagiotis Tolias, Maximilian P. Böhme, Luke B. Fletcher, Thomas Gawne, Frank R. Graziani, Dominik Kraus, Michael J. MacDonald, Zhandos A. Moldabekov, Sebastian Schwalbe, Dirk O. Gericke & Jan Vorberger - Nature Communications 16, 5103 (2025)

The study of matter at extreme densities and temperatures has emerged as a highly active frontier at the interface of plasma physics, material science and quantum chemistry with relevance for planetary modeling and inertial confinement fusion. A particular feature of such warm dense matter is the complex interplay of Coulomb interactions, quantum effects, and thermal excitations, making its rigorous theoretical description challenging. Here, we demonstrate…

Anthony Gandon, Alberto Baiardi, Max Rossmannek, Werner Dobrautz, and Ivano Tavernelli - PRX Quantum 6 (2025)

Exploiting inherent symmetries is a common and effective approach to speed up the simulation of quantum systems. However, efficiently accounting for non-Abelian symmetries, such as the global SU(2) spin symmetry, remains a major challenge. In fact, expressing total-spin eigenstates in terms of the computational basis can require an exponentially large number of coefficients…

Get to know our teams

Theory of Complex Systems

Prof. Thomas D. Kühne
Chemical as well as physical processes are intrinsically associated with large length and time scales.
VIEW MORE

Earth System Science

Prof. Dr. Justin Calabrese
The Earth System Science research team at CASUS tackles problems at the interface spatial ecology, hydrology, and anthropogenic change to understand the dynamics of biodiversity in the Anthropocene
VIEW MORE

Machine Learning for Materials Design

Dr. Attila Cangi
The Machine Learning for Materials Design department develops scalable machine learning methods that accelerate first-principles simulations of electronic and atomistic structures, with the overarching goal of discovering and designing novel materials.
VIEW MORE

Frontiers of Computational Quantum Many-Body

Dr. Tobias Dornheim
Our group develops novel methods and concepts to tackle problems from quantum many-body theory.
VIEW MORE

Scientific Computing Core

Dr. Andreas Knüpfer
The department supports CASUS researchers in all aspects of scientific computing and data-driven research. It also conducts research on specific topics of its own.
VIEW MORE

Computational Radiation Physics

Dr. Michael Bussmann
The group models, simulates and visualizes the dynamics of particles and radiation phenomena that are of interest when investigating the physics of laser particle acceleration.
VIEW MORE

AI 4 Quantum

Dr. Werner Dobrautz
We develop a synergistic high-performance and quantum computing approach aided by novel artificial intelligence/deep machine learning methods to enable the computational study of complex quantum systems relevant to the green energy transition.
VIEW MORE

Theoretical Chemistry

Dr. Agnieszka Beata Kuc
In the Theoretical Chemistry group, we explore innovative materials, with a particular emphasis on two-dimensional systems, for use in energy storage and generation, catalysis, isotope separation, and nano(opto)electronic devices.
VIEW MORE

We are not making science for science

We are making science for the benefit of humanity

Françoise Barré-Sinoussi

Mathematical Foundations of Complex System Science

Dr. Michael Hecht
The beauty and fascinating enigmatic nature that complex systems embody might be the driving force behind the ambitions of many scientists in their realm of scientific research.
VIEW MORE

Dynamics of Complex Living Systems

Dr. Ricardo Martínez-García
In the Dynamics of Complex Living systems team, we are interested in standing self-organization and emergence in living systems across scales.
VIEW MORE

Machine Learning for Infection and Disease

Dr. Artur Yakimovich
Machine Learning for Infection and Disease (MLID) group aims to develop novel computational methods to facilitate our understanding of Infection Biology and Disease Biology.
VIEW MORE