EU Regional School - Uciński Seminar

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

Prof. Dariusz Uciński Ph.D. - Optimum Experimental Design for Distributed Parameter System Identification

Institute of Control and Computation Engineering, University of Zielona Góra, Poland


The impossibility of observing the states of distributed parameter systems over the entire spatial domain raises the question of where to locate measurement sensors so as to estimate the unknown system parameters as accurately as possible. Both researchers and practitioners do not doubt that making use of sensors placed in an ‘intelligent’ manner may lead to dramatic gains in the achievable accuracy of the parameter estimates, so efficient sensor location strategies are highly desirable. In turn, the complexity of the sensor location problem implies that there are few sensor placement methods which are readily applicable to practical situations. What is more, they are not well known among researchers. The aim of the minicourse is to give account of both classical and recent original work on optimal sensor placement strategies for parameter identification in dynamic distributed systems modeled by partial differential equations. The reported work constitutes an attempt to meet the needs created by practical applications, especially regarding environmental processes, through the development of new techniques and algorithms or adopting methods which have been successful in akin fields of optimal control and optimum experimental design. While planning, real-valued functions of the Fisher information matrix of parameters are primarily employed as the performance indices to be minimized with respect to the sensor positions. Particular emphasis is placed on determining the ‘best’ way to guide moving and scanning sensors, and making the solutions independent of the parameters to be identified. A couple of case studies regarding the design of air quality monitoring networks are adopted as an illustration aiming at showing the strength of the proposed approach in studying practical problems. The course will be complemented by a discussion of more advanced topics including the related problem of optimum input design and the Bayesian approach to deal with the ill-posedness of parameter estimation.


EU Regional School - Kiendl Seminar

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

Prof. Dr. Josef Kiendl - TBA

Department of Marine Technology, Norwegian University of Science and Technology, Norway




SSD - Elber Seminar

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

Prof.Gershon Elber, Ph.D. - Volumetric Representations: the Geometric Modeling of the Next Generation

Computer Science Department, Technion - Israel Institute of Technology, Israel


The needs of modern (additive) manufacturing (AM) technologies can be satisfied no longer by boundary representations (B-reps), as AM requires the representation and manipulation of interior fields and materials as well. Further, while the need for a tight coupling between design and analysis has been recognized as crucial almost since geometric modeling (GM) has been conceived, contemporary GM systems only offer a loose link between the two, if at all.

For about half a century, (trimmed) Non Uniform Rational B-spline (NURBs) surfaces has been the B-rep of choice for virtually all the GM industry. Fundamentally, B-rep GM has evolved little during this period. In this talk, we seek to examine an extended volumetric representation (V-rep) that successfully confronts the existing and anticipated design, analysis, and manufacturing foreseen challenges. We extend all fundamental B-rep GM operations, such as primitive and
surface constructors and Boolean operations, to trimmed trivariate V-reps. This enables the much needed tight link to (Isogeometric) analysis on one hand and the full support of (heterogeneous and anisotropic) additive manufacturing on the other.

Examples and other applications of V-rep GM, including AM and lattice- and micro-structure synthesis and heterogeneous materials will also be demonstrated.


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


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