SSD - Brockmann Seminar

Location: AICES Seminar Room 115, 1st floor, Schinkelstr. 2, 52062 Aachen

Dr. Matthias Brockmann- Internet of Production – Need for Simulation and Data Science for the Future of Production

Chair of Machine Tools, RWTH Aachen University


The vision of the „Internet of Production“ describes the common research
roadmap of RWTH Aachen University concerning Industrie 4.0 within the
Cluster of Excellence EXC 2023 at RWTH Aachen University.
Due to highly sophisticated, specialized models and data in production,
Digital Twins – in their meaning as full digital representations – are
neither computationally feasible nor purposeful.
The concept of Digital Shadows will provide cross-domain data access in
real time by combining reduced engineering models and production data
This seminar will provide an overview of actual research topics, as well
as needs and requirements for data science and simulation in the context
of the Internet of Production.
Core concepts like the Digital Shadow, a new reference infrastructure and
approaches for a new level of cross-domain collaboration will be
Practical examples for agile manufacturing will be presented by means of
chosen use cases from different fields of production technology.

EU Regional School - Ham Seminar

Location: AICES Seminar Room 115, 1st floor, Schinkelstr. 2, 52062 Aachen

Dr. David Ham - Automated Simulation from Equations to Computation with Firedrake

Department of Mathematics, Imperial College London, UK


Creating simulations by numerically solving PDEs often requires large amounts of complex low-level code which is hard to write, hard to debug, and hard to change. It doesn’t need to be like that! In this tutorial we’ll present the Firedrake automated finite element system. Firedrake users write finite element problems mathematically using the Unified Form Language (UFL) embedded in Python. High performance parallel operator and residual assembly is automatically generated using advanced compiler technology. Firedrake integrates with the PETSc framework to provide a full suite of sophisticated linear and nonlinear solvers. In this hands-on Jupyter-based tutorial, you will have the chance to solve linear and nonlinear PDEs using Firedrake and try out some of its advanced features, including: 

  • Linear and nonlinear problems with Dirichlet and Neumann boundary conditions
  • Mixed systems, composable Schur complement, multilevel, and operator-based preconditioners
  • Automated solution of time-dependent adjoint PDEs using dolfin-adjoint

Information: Please bring a laptop with you. You'll work with:


SSD - Riedel Seminar

Location: AICES Seminar Room 115, 1st floor, Schinkelstr. 2, 52062 Aachen

Prof. Dr. Morris Riedel - Parallel and Scalable Machine Learning Co-Design of a Modular Supercomputing Architecture

Institute for Advanced Simulation (IAS), Research Centre Jülich 


The fast training of machine learning models and innovative deep learning networks from increasingly growing large quantities of scientific and engineering datasets requires high-performance computing (HPC). Modern supercomputing technologies such as those developed within the European DEEP-EST project provide innovative approaches w.r.t. processing, memory, and modular supercomputing usage during training, testing, and validation processes. This talk illustrates why and how parallel processing is a key enabler for a wide variety of machine and deep learning algorithms today. Examples include commercial, scientific and engineering applications that leverage parallel and scalable feature engineering, density-based spatial clustering of applications with noise (DBSCAN), and convolutional neural networks (CNNs). The talk concludes with a short overview of the new Helmholtz Artificial Intelligence Cooperation Unit (HAICU) and its local setup at Forschungszentrum Juelich.

SSD - Picasso Seminar

Location: AICES Seminar Room 115, 1st floor, Schinkelstr. 2, 52062 Aachen

Prof. Marco Picasso, Ph.D. - Adaptive Finite Element with Large Aspect Ratio

Mathematics Institute of Computational Science and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland



Adaptive finite elements with large aspect ratio have shown to be extremely efficient whenever the solution to the underlying pde has internal or boundary layers. The adaptive criteria can be based on anisotropic a posteriori error estimates, the involved interpolation constants being aspect ratio independent.


In this talk, some a posteriori error estimates and adaptive algorithms will be presented on academic problems (elliptic, parabolic and hyperbolic pde's). Numerical results on more challenging CFD problems will also be discussed.


SSD - Hosters Seminar

Location: AICES Seminar Room 115, 1st floor, Schinkelstr. 2, 52062 Aachen

Dr. Norbert Hosters - Spline-based Methods for Fluid-structure Interaction



The introduction of isogeometric analysis (IGA), made it possible to directly exploit the
favorable geometric properties of NURBS for numerical analysis. The method has become
wide-spread in structural mechanics. However, parametrizing complex three-dimenional
domains — as needed for CFD — using closed volume splines can be challenging. NURBS-
enhanced finite elements (NEFEM)  can be a viable alternative. Both methods together
lead to a geometrically compatible coupling interface for fluid-structure interaction (FSI). Within a partitioned FSI
method, the necessary projection methods simplify due to the matching geometry; 
while at the same time increasing accuracy. But the spline-based description not only offers 
advantages in linking the individual fields. The spline-based boundary description is also 
beneficial for problems of FSI with additional complexities, e.g. free surfaces or contact.
In the talk the theoretical background of the applied spline-based FSI solver will be presented. 

The numerical advantages of the presented combination of isogeometric analysis and the NURBS-enhanced 

finite element method is demonstrated with simple stationary and transient test cases. With more
complex FSI test cases, additional advantages of spline-based methods are presented.