SSD - Elber Seminar
Prof.Gershon Elber, Ph.D. - Volumetric Representations: the Geometric Modeling of the Next Generation
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
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
Dr. David Ham - Automated Simulation from Equations to Computation with Firedrake
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: https://www.firedrakeproject.org
SSD - Riedel Seminar
Prof. Dr. Morris Riedel - Parallel and Scalable Machine Learning Co-Design of a Modular Supercomputing Architecture
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
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.