All PhD candidates presented in a short talk the recent progress of their research projects.

Trainer: Dr. Hans-Peter Eckle

Trainers: Constanze Schwärzer-Dutta & Robel Afeworki Abay

Trainer: Dr. Simon Golin

Talk by Prof. Dr. Markus Bär (Physikalisch-Technische Bundesanstalt, Berlin) on ‚Modelling dynamics and control in cardiac systems and living fluids’.

Talk by Prof. Dr. Lars Ruthotto (Emory University, Atlanta, USA) on ‘An Introduction to Generative Modeling’

Talk by Prof. Dr. George Karniadakis (Brown University, Providence, USA) on ‘Physics-Informed Machine Learning for Diverse Applications in Science and Engineering’.

Talk by Prof. Dr. Alexandre Tkatchenko (University of Luxembourg) on ‘Machine Learning Force Fields.’

Talk by Prof. Dr.-Ing. Philipp Geyer (Technische Universität Berlin) on ‘Using data-driven models generated by component-based machine learning to replace simulation in energy efficient building design’.

On November 03, 2021 the introductory intensive course period begins. The courses offered provide basic competences in the main research areas of our RTG. Students are familiarised with the mathematical foundations and application foci that are crucial for the projects in order to bridge interdisciplinary language gaps and generally strengthen the cohesion of the cohort. Participation in these courses is mandatory for the cohort.

From October 18-20, 2021 our second interdisciplinary welcome week took place at TU Berlin. The three day program included informational talks on the scientific and organizational side of our RTG, presentations of the PIs involved in our RTG, a soft skill course on ‘academic presentation skills’ by Lesley-Anne Weiling, scientific speed meeting and a poster session where the PhD students presented their master thesis (2nd cohort) and their PhD projects (1st cohort) to the group.
The program can be downloaded here. Interdisciplinary Welcome Week_2021_Web

By Prof. Dr. Cecilia Clementi (FU Berlin).

By Prof. Dr. Gabriele Steidl (TU Berlin).

By Prof. Dr. Volker John (WIAS & FU Berlin).

By trainer Dr. Simon Golin.

This event took place online.

By Prof. Dr. Tobias Breiten (TU Berlin).

By Prof. Dr. Abdulla Ghani (TU Berlin).

By Prof. Dr. Barbara Zwicknagl (HU Berlin).

The workshop was held by Mark Edwards.

Prof. Kevin Carlberg from the University of Washington, Seattle, USA, will give a 1 hour talk on ‘Nonlinear model reduction: using machine learning to enable rapid simulation of extreme-scale physics models’ as part of our this years online Winter School.

Prof. Gianluigi Rozza from SISSA, Trieste, Italy will give a 1 hour webinar on ‘State of the art and perspective of reduced order methods in computational fluid dynamics and applications’ as part of our this years online Winter School.

All PhD candidates presented in a short talk the recent progress of their research projects.

This event has been cancelled due to the corona virus.

Trainer: Mark Edwards

Due to the Coronavirus crises the event is cancelled!

Due to the Coronavirus crises the retreat is cancelled!

by Prof. Dr. Begüm Demir (TU Berlin)

by Prof. Dr. Sabine Klapp (TU Berlin)

Dr. Alexander von Lühmann (Boston University)

by Prof. Dr. Hans-Jörg Vogel (Helmholtz Centre for Environmental Research – UFZ)

Machine Learning meets Numerical Analysis of PDEs

Our first DAEDALUS Winter School took place from December 02-06, 2019 nearby Berlin at the Seehotel Zeuthen. Nine invited speakers gave compelling lectures on the subject of ‘Machine Learning meets Numerical Analysis of PDEs’ (see list below). Further to the DAEDALUS students, participants from Deutsches Elektronen-Synchrotron DESY (Zeuthen), SISSA – Scuola Internazionale Superiore di Studi Avanzati (Trieste, Italy) and ETH Zurich attended the school. Between classes and during the poster session, students, lecturers and DEADALUS staff had the possibility to discuss the topics and/or aspects of their work further.

Some impressions of the winter school

Invited Speakers:

• Prof. Dr. Markus Bachmayr (Johannes Gutenberg-Universität Mainz): ‘Adaptive Sparse and Low-Rank Approximations for High-Dimensional Problems’
• Prof. Dr. Arnulf Jentzen (University of Münster)
• Prof. Dr. Gitta Kutyniok (Technische Universität Berlin): ‘Beating the Curse of Dimensionality: A Theoretical Analysis of Deep Neural Networks and Parametric PDEs’
• Prof. Dr. Nathan Kutz (University of Washington): ‘Data-driven discovery of governing equations and laws in engineering, physics and biology’
• Prof. Dr. Ivan Oseledets (Skoltech Faculty Moscow): ‘Constructive methods for multivariate function approximations using tensors and deep learning’
• Prof. Dr. Philipp Petersen (University of Vienna): ‘A primer on approximation theory of neural networks’
• Leon Sallandt (Technische Universität Berlin): ‘A simple implementation of the policy iteration for solving the HJB equation’
• Prof. Dr. Tobias Schaeffter (Physikalisch-Technische Bundesanstalt Berlin): ‘Quantitative Magnetic Resonance Imaging’
• Prof. Dr. Reinhold Schneider (Technische Universität Berlin): ‘Hamilton Jacobi Bellman Equation and Feedback Control of Partial Differential Equations’

Schedule Winter School PDF

by Dr. Özgür Gürçan (CEDEX, France)

by Prof. Dr. Holger Boche (TU München)

by Mr. Marco Congedo (CNRS, University Grenoble Alpes)

by Professor Matthew Juniper (University of Cambridge)

Our first research retreat took place from May 02-03, 2019 in Berlin at DAS SCHMÖCKWITZ. The PhD students gave research presentations on their progress during the first few months of their PhDs. Afterwards, the supervisory committees had the chance to give feedback and discuss the projects. Further, each student presented a high impact paper to the group. A program of our retreat can be downloaded here.

Trainer: Dr. Olaf Hars

Trainer: Constanze-Schwärzer Dutta and Gülden Ediger

On October 29, 2018 an Introductory Intensive Course Period started for six weeks. The offered courses provided basic competencies in the main research  areas of our RTG. The key goals of this intensive teaching period were to familiarize the students with the mathematical foundation and application foci which are crucial for the projects, to continue bridging interdisciplinary language gaps and in general to strengthen the cohesiveness of the cohort. Participating in these courses is mandatory for the cohort. The six courses were devoted to the following topics:

• Data acquisition and analysis/machine learning
Basic principles of clustering, deep neural networks, data management, linear regression, learning theory, statistical estimation, and support vector machines

• Optimization and optimal control
Basic principles of adjoint equations, first and second order optimality conditions, maximum principle, and nonlinear optimization

• Sparsity and low-rank approximation
Basic principles of compressed sensing, manifold learning, matrix completion, and tensor approximation

• Uncertainty quantification
Basic principles of frequentist and Bayesian approaches, sources of uncertainties such as parameter uncertainty, and uncertainty propagation

• (Partial) differential equation based-modeling and solvers
Basic principles of data assimilation, Galerkin approaches, model reduction, and reduced basis method

• Multiscale problems
Basic principles of adaptive approaches, multiscale modeling, multigrid, and wavelet multiresolution analysis

The courses were taught by experts such as PIs, additionally involved scientists, post-docs, or visiting researchers.