CASUS – Center for Advanced Systems Understanding

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.

Innovation in Science

Helmholtz AI initiative

funds protein design project. CASUS is part of the successful initiative called PROFOUND

The Helmholtz Association, Germany’s largest research organization, has announced the winners of its second Helmholtz Foundation Model Initiative (HFMI) call.

New deep learning architecture enables higher efficiency compared to widely used methods

It is the computational processing of images that reveals the finest details of a sample placed under all kinds of different light microscopes.

Intelligent swarm

Working in a team is also relevant for drones

Uncrewed Aerial Vehicles (UAVs), commonly known as drones, are omnipresent and have grown in popularity due to their wide potential use in many civilian sectors

New method for interactive microscopy in 3D

Making experiments with live samples more insightful

CASUS scientist receives prestigious ERC Starting Grant

Young Investigator Dr. Tobias Dornheim aims to tackle one of the fundamental computational bottlenecks in physics, chemistry, and related disciplines.

Tobias Dornheim receives Lem Research Prize

Wrocław University of Science and Technology’s young talent award goes to CASUS researcher

Upcoming

CASUS Drone Adventure Workshop

06/02/25 | 9:00 - 12:00 / 13:00 - 16:00

CASUS Drone Adventure Workshop

We are inviting kids aged 12-16 from Poland and Germany to a unique workshop where they will build their own drones in 4 groups and learn how to control them! Drone building is learning through fun – practical tasks await you!

High-performance computing workshop

29/01/25 | 10:00

CASUS Workshop & Seminars

High-performance computing (HPC) clusters are used for large-scale data processing and data analysis, fine-grained parallel calculations and simulations of ever increasing fidelity.

Publications

Probing iron in Earth’s core with molecular-spin dynamics

Svetoslav Nikolov, Kushal Ramakrishna, Andrew Rohskopf, Mani Lokamani, Julien Tranchida, John Carpenter, Attila Cangi, Mitchell A. Wood - PNAS December 12, 2024

Dynamic compression of iron to Earth-core conditions is one of the few ways to gather important elastic and transport properties needed to uncover key mechanisms surrounding the geodynamo effect. Herein, a machine-learned ab initio derived molecular-spin dynamics (MSD) methodology with explicit treatment for longitudinal spin-fluctuations is utilized to probe the dynamic phase-diagram of iron…

A high-order fully Lagrangian particle level-set method for dynamic surfaces

Lennart J Schulze, Sachin KT Veettil, Ivo F Sbalzarini - Journal of Computational Physics, Volume 515, 15 October 2024, 113262

We present a fully Lagrangian particle level-set method based on high-order polynomial regression. This enables meshfree simulations of dynamic surfaces, relaxing the need for particle-mesh interpolation. Instead, we perform level-set redistancing directly on irregularly distributed particles by polynomial regression in a Newton-Lagrange basis on a set of unisolvent nodes…

Quantum delocalization, structural order, and density response of the strongly coupled electron liquid

Tobias Dornheim, Panagiotis Tolias, Jan Vorberger, Zhandos A. Moldabekov - EPL, 147 (2024) 36001

We investigate the impact of electronic correlations and quantum delocalization onto the static structure factor and static density response function of the strongly coupled electron liquid. In contrast to a classical system, the density response of the electron liquid vanishes on small length scales due to quantum delocalization effects, which we rigorously quantify in terms of imaginary-time correlation functions and dynamic Matsubara response functions…

Spatial computational modelling illuminates the role of the tumour microenvironment for treating glioblastoma with immunotherapies

Blanche Mongeon, Julien Hébert-Doutreloux, Anudeep Surendran, Elham Karimi,Benoit Fiset,Daniela F. Quail, Logan A. Walsh, Adrianne L. Jenner, Morgan Craig - npj Systems Biology and Applications volume 10, Article number: 91 (2024)

Glioblastoma is the most common and deadliest brain tumour in adults, with a median survival of 15 months under the current standard of care. Immunotherapies like immune checkpoint inhibitors and oncolytic viruses have been extensively studied to improve this endpoint. However, most thus far have failed. To improve the efficacy…

Our focus

Theory of Complex Systems

Prof. Thomas D. Kühne

Chemical as well as physical processes are intrinsically associated with large length and time scales.

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

Machine Learning for Materials Design

Dr. Attila Cangi

We develop scalable machine learning frameworks to improve density functional theory simulations, connect microscopic and mesoscopic simulations, and model advanced material properties.

Frontiers of Computational Quantum Many-Body

Dr. Tobias Dornheim

Our group develops novel methods and concepts to tackle problems from quantum many-body theory.

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.

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.

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.

We are not making science for science

We are making science for the benefit of humanity

Françoise Barré-Sinoussi

Mathematical Foundations of Complex System Scienc

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.

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.

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.