Monday 25.08.2025 - Friday 29.08.2025
an International Summer School
organized by the
Dresden Center for Computational Materials Science (DCMS), the Dresden Center for Intelligent Materials (DCIM) and the D³ Research Training Group 2868
The arrising of data science and machine learning into the field of material science have deeply modified the discipline. This brisk upcoming redefined both the tasks and the the objectives, leading to the development of new methods and considerations. This summer school offer an extensive multidisciplinary insight on these new developpements, extending from experimental characterization to machine learning, including alloy design and mechanical modeling.
The Research Training Group 2868 "Data-Driven Design of resilient meta materials" (D³) aims at realizing a fully digital, data-driven approach to design meta-materials for the sectors of energy, medicine and mobility. Together with the Dresden Center for Materials Science (DCMS) and the Dresden Center for Intelligent Materials (DCIM), we organize this year's joint summer school from Monday August 25st to Friday August 29th 2025 in Dresden.
Internationally renowned speakers will teach their perspectives in a 5-day workshop in the spectrum of data-driven design of materials and structures. The lectures will be extended by hands-on sessions to achieve a greater understanding of the last scientifical developpements. The whole will finally be encompassed into an extensive social program.
Professor Mohr is conducting extensive research on the design and manufacturing of novel architected materials via the development of experimentally-validated computational models. More specifically, this includes the study of the thermo-mechanical behaviour of materials at high strain rates, crashworthiness as well as the mechanics of constructed cellular materials. Having obtained his P.h.D from the Massachusetts Institute of Technology, he had the opportunity to conduct research at the École Polytechnique as well as at MIT. He is currently full professor at ETH Zürich and head of the Institute of Virtual Manufacturing.
Professor Bessa is leading a research group for the development of artificial intelligence-based methods to design novel materials and structures with innovative properties This includes multi-scale modeling of material as well as material optimization via physics-informed machine learning. He achieved his PhD in Northwestern University and pursued his research with a short postdoctoral position at Caltech as well as a position at TU Delft. He is now an associate professor at Brown University focusing on computational mechanics and machine learning.
Professor Leineweber is known for his work on various aspects of solid-solid phase transformations, especially in the case of steel/cast iron as well as various types of intermetallics. He is currently full professor at the TUB Freiberg, where he heads the group "Applied Materials Science" at the Institute of Material Science.
Professor Kästner is focusing on the development of data-driven analysis and techniques for material multi-scale modeling along with the experimental characterization of additively manufactured materials. His research interests encompass a large scope of subjects, from inverse material design to damage and fracture analysis. He is a full professor at the TU Dresden where he received his PhD in 2015. Among his projects belongs the research training group D³ on data-driven design of metamaterials aiming to develop novel resilient materials.
The research training group D³ - Data-Driven design of metamaterials - is aiming to the exploration of novel cross-scale materials and structures achieving enhanced mechanical performances. This challenge is tackled by an interdisciplinary team involving experts in computational mechanics, data and computer science, materials science, mechanical engineering, mathematics, and physics. To achieve its objectives, the D³ adresses various challenges, from the structure optimization to the functionalization, including the alloy design and the mechanical testing.
Perspectives in Data-Driven Material Design is first of all a school. Its aim is to teach 5 days long the novel concepts and methods uprising in material science to a large public of master and PhD students. We consequently believe that it should have a programme proper to a school and not to a conference, allowing a real grasp of the topics beyond a simple overview.
The summer school will be organized around 5 main courses presented by internationally renowned scientists and covering various disciplines of material science. Each course will be decomposed into two lectures, offering both an introduction as well as advanced considerations of each topic. These two lectures can be expanded by a hands-on session to develop a concrete understanding of the concepts. Besides these courses, one-shot lectures will propose novel and broader insight into the current research.
We do strongly believe that a summer school is not only a place to learn, but also a place to meet other scientists. After mutually meeting each other during the poster session, we offer numerous social events, including a visit of the Dresden city, to get in touch with new persons and expand your professional and personal contacts.
Applications will be accepted until Monday, May 19th 2025, at 10 am (CET). The summer school targets Master students and early PhD students
working on or interested in computational and experimental materials science and topics related to synthesis, modelling and integration of
intelligent/smart/active materials, novel fabrication methods and high-throughput methods.
To apply for the school the following documents are needed and have to be uploaded as a PDF in the online form (see below).
The summer school will take place
at the Görges Bau on the TU Dresden main campus.
Suggestions for accommodation: