Transdisciplinary Physics


With its domain Transdisciplinary Physics, the NFDI4Phys consortium enables and strengthens bridges between Physics and other disciplines near and far. It gathers all communities and collaborators with whom members of our consortium interact in reaching our across discipline and spanning a large part of the DFG panel classification scheme. It is built around the unifying power of Physics and its search for common principles and universal laws in systems across a wide range of application domains. We do not claim to cover all interactions of Physics with other disciplines but certainly represent some of the conceptually most important.  Specific aims and objectives in RDM may be found under each discpline page link.

Systems Biology & Systems Medicine

  • Prof. Dr. Marc-Thorsten Hütt, Jacobs University

The vision of the Systems Biology and Systems Medicine segment of the Transdisciplinary Physics domain in NFDI4Phys is to bring the unique point of view of Physics to the rich resources of high-throughput data in Biology and Medicine. Our goals are to make tools from Statistical Physics available for use in biological and medical databases, to collect and host tools facilitating transdisciplinary access to these databases, and to educate researchers in Physics enabling them to make use of these rich data resources



  • Prof. Dr. Wolfgang Marwan, Otto-von-Guericke-Universität Magdeburg

Biophysical analysis of the structure and dynamics of gene regulatory networks controlling cell fate decisions and the differentiation of living cells requires the integration of complex, high throughput datasets of markedly different formats and data types. Through mutual interactions with partners of the NFDI4Phys consortium, we hope to develop innovative concepts leading to a unifying framework which can cope with this unavoidable heterogeneity in data formats. We are also happy to contribute and elaborate approaches for the automatic interpretation of experimental time series data sets with the help of algorithms that compute the structure and dynamics of regulatory networks in terms of executable models directly from the data sets. In general, such executable models represent the structure and dynamics of causal interactions within a system and may be relevant for systems of different complexity, from molecular networks up to systems in sociology or economy.


Biological Psychology

  • Prof. Dr. Thorsten Fehr, Universität Bremen

Biological Psychology, as one of the main sub domains of Psychology, provides an inherent transdisciplanary bridge between the humanities, social sciences and the natural sciences as it adumbrates and links a variety of aspects from psychological, biological, physical and other disciplines. One sub-domain of the Biological Psychology, the comparative psychology, compares the physiological principles underlying behaviour and experience between species. Different methodological approaches come into play that cover a variety of levels between micro-(i.e., subnuclear) and macroscopic (i.e. complex system interaction) perspectives, such as magnetic resonance imaging, positron emission tomography, electroencephalography, electrocardiography, electromyography, and many more.

Artifical Intelligence

  • Prof. Dr. Dr. h.c. Frank Kirchner, DFKI & Universität Bremen (Robotics)
  • Prof. Dr. Philipp Slusalleck, DFKI Saarbrücken (Agents and simulated Reality)

Computational Social Sciences

  • Prof. Dr. Uwe Engel, Universität Bremen

Computational social science (CSS) is a dynamically developing discipline in the intersection of data science and social science. A lot of social interaction and interpersonal communication takes place exclusively on the internet. This produces digital trace data in the form of texts and/or behavioral marks, which people leave when surfing the web. These digital survey data accumulate on a grand scale. It is the original four Vs of Big Data (volume, velocity, variety, veracity) that make such great demands on a contemporary management of the digital research data in CSS. A transdisciplinary element is provided by “variety”: the sensing of physical data while collecting survey data [Bosse & Engel 2019]. In addition, CSS consists of more elements than big data and data analytics. CSS is also deeply rooted in mathematical modeling and simulation in sociology. Social simulation is a third supporting pillar of CSS with a transdisciplinary core: its multilevel approach towards social complexity; a concept that designates the genuinely emergent aggregate features and inherent regularities of social systems and their major constituents such as social groups, networks, and individual agents. A challenge which is currently tackled in this context is the data-driven performance of social simulation to achieve more than images of artificial societies. [Engel et al. 2021]

Philosophy of Law

  • Prof. Dr. Lorenz Kähler, Universität Bremen
  • Prof. Dr Hans-Günther Döbereiner, Universität Bremen

Empirical Legal Studies are concerned with general legal phenomena in society. These are expected to follow patterns that correspond to similar phenomena as studied, e.g. in the Domain Socio-economic Systems. In order to conduct our studies, we will benefit from discussions with colleagues from this domain and with members of sections Biological Psychology, Computational Social Sciences, and Digital Humanities within the Domain Transdisciplinary Physics. We will employ the survey methodology available via task area Surveys to conduct our studies remotely. We are looking forward to a FAIR handling of our remote data. Apart from similar disciplinary topics, we share the concern and challenges of data privacy and security.  We will potentially utilize  data available via interfacing with consortia which provide data from business studies, economics and related fields, integrating expertise from both research and infrastructure. We will utilize integrated management of unstructured (Big) data and algorithms of artificial intelligence provided by interfaced consortia. In addition to pioneering FAIR legal data management already available from field experiments or acquired a new by our surveys, we will uncover hidden correlations in qualitative legal texts. In collaboration with the task areas FAIR Laboratory and Metadata and Ontologies , we will attempt automated semantic analyses of legal documents and built up relevant ontologies. Semantic analysis, which we conduct under close guidance by the SEM working group of the FDO forum, lowers the divide between qualitative and quantitative data. Applying a semantic metric to a qualitative text quantifies it to a certain degree and enables the digital transformation to a FAIR Digital Object which is available on a federated repository. We will apply for  additional DFG  funds for a research project enabled by integrative RDM within NFDI.




Material Humanities

In the Cluster of Excellence »Matters of Activity« researchers from more than 40 disciplines are investigating the activity of matter. Our central vision is to rediscover analog processes in the age of the digital within the activity of images, spaces and materials driven by the integrative nature of the Humanities and with the Natural Sciences and the different Design disciplines. Biology and technology, mind and material, nature and culture intertwine in a new way. In this context, the interdisciplinary research and development of sustainable practices and structures is a central concern in areas such as architecture and soft robotics, textiles, materials and digital filters, and surgical cutting techniques. Objects and materials are not thought of as passive and unadaptable, but rather as active, changeable and recyclable materials. The three closely linked research units »Practices«, »Structures« and »Code« focus on three different approaches to material activity: from the basic level of cultural material practices and the material’s inherent active structures to the challenging idea of a novel kind of material code. Thus, Material Humanities designates a new field which adds to Material Sciences a cultural aspect in contrast to the often purely technological view of the Natural Sciences. It adds a totally new perspective to FDM as qualitative concepts from the Humanities guide quantitative  workflows of the Natural Sciences which constitutes a  major challenge for the construction of metadata ontologies defining the boundary condition of the creational process inherent in material production.

Material Sciences (TBD)

  • Christoph Eberl

process-driven non-equilibriun




Digital Economy

  • Prof. Dr. Lars Hornuf, Universität Bremen
  • Prof. Dr. Martin Kocher, Wien
Historically, economic research has considered three factors of production—capital, labor, and land. Another production factor that can be derived from the original three is technological change. A fundamental resource that drives technological change is data, which has famously been termed the ’new oil’ fueling the economy. Data is needed to drive new technologies such as artificial intelligence, autonomous vehicles, or decision support systems such as chatbots. Data is a double-edged sword though. From a utilitarian perspective, it can improve economic activities by predicting market demands more accurately and making production processes more efficient. Deontological ethics cautions that the absence of data privacy and data security can make markets and society less free. As of today, little research as been conducted that studies individual preferences of data privacy and data security in various domains of life. The project Digital Economy seeks to conduct basic experimental research to better understand the functioning of the digital economy and the preferences of the individual actors therein.

Digital Architecture

  • Prof. Liss C. Werner, Institute of Architecture, Technische Universität Berlin

The advent of Digital Architecture in the 1960s has influenced the discipline of architecture insofar that the creation of buildings, cities, and regions relocated from an analog physical design process to one that was partially supported by digital computation processes (Conway’s Game of Life. MIT Architecture Machine). The late 1990s offered the first steps into CADCAM, known as Computer-Aided Drawing and Manufacturing. In comparison, Digital Architecture in the 2020s engages with the evolution of the very design process as an in-silico computational generation of architecture that integrates social, biological and material intelligence alike. The rising understanding of ecology as computational complexity paired with climatic and population growth issues challenges the discipline. It requires comprehensive sets of disparate data. Digital Architecture aims at designing the transformation of regions, cities, buildings, and construction components as a partial data-driven cybernetic design process. The goal is to learn about the impact and causal relationships of intelligent materials, sensors, biology, AI, biodiversity, population growth, social structures, and climate phenomena on the design process and ultimately the built form – terrestrial and extraterrestrial.

Climate Impact Research

  • Prof. Dr. Dr. h.c. mult. Jürgen Kurths, Potsdam Institute for Climate Impact Research (PIK)

It is PIK’s twofold mission to advance the scientific frontier on interdisciplinary climate impact research for global sustainability and to contribute knowledge and solutions for a safe and just climate future. The Research Department 4 “Complexity Science” investigates the properties of the natural and societal complex systems in the realm of climate change and its impacts. In particular, research in RD4 intends to advance the understanding of the complex nature of these systems in order to find new principles that can improve and complement existing modelling and empirical approaches. RD4 explores new territory to find principles, analysis methods, and modelling techniques. With guidelines for Data Management Plans (DMP), PIK provides for its scientists an elaborated framework in terms of data usage, maintenance and dissemination settings to ensure data transparency.


  • Prof. Dr. Rolf Drechsler, DFKI & Universität Bremen
  • Dr. Lena Steinmann, Data Science Center, Universität Bremen

The cross-cutting discipline data science opens up new possibilities to extract knowledge from heterogeneous, complex datasets using modern analysis methods such as machine learning. Hence, data science is regarded as key discipline of the digital era. Data science requires high-quality data and, thus, a sustainable research-data management as envisioned by the NFDI. The Data Science Center (DSC) is an interdisciplinary institute that acts as focal point for data-driven research and data science at the University of Bremen. Our goal is to strengthen data science in research, education, and application across all disciplines as well as to advance scientific discoveries through cross-disciplinary collaborations. Within the context of NFDI4Phys, we aim to promote data literacy across all status groups and support the use of data science methods, especially from the field of artificial intelligence, in the physics community.


  • Prof. Dr. Klaus Pawelzik, Universität Bremen
  • Prof. Dr. Udo Ernst, Universität Bremen
  • Prof. Dr. Andreas Kreiter, Universität Bremen

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