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.
SSD - Hosters Seminar
Dr. Norbert Hosters - Spline-based Methods for Fluid-structure Interaction
The numerical advantages of the presented combination of isogeometric analysis and the NURBS-enhanced
SSD - Lipparini Seminar
Prof. Filippo Lipparini, Ph.D. - Multiscale Modeling: a Chemical Perspective to an Interdisciplinary Problem
Computational chemistry is the branch of chemistry that uses models and computer simulations in order to predict or rationalize the molecular behavior of chemical systems. Methods based on quantum mechanics are nowadays extensively used in order to study molecular properties, structures or reactivity and are becoming a standard technique in the toolbox of a chemist. Many different methods exist, that differ in accuracy and applicability, due to their computational cost. Unfortunately, the size of the systems to which such methodologies can be applied is limited. Processes that involve large biomolecules, or that happen in solution, can not be described in a naive way by just increasing the size of the model system. Focused multiscale models, that divide the system in a core, where the interesting process mainly happens, and an environment, which plays a spectator role to the process, but influences it by tuning the properties of the core, are one of the most successful strategies to deal with such complex phenomena. In this presentation, I will quickly present two of such multiscale models, namely, continuum solvation models and QM/MM models, and describe some of the challenges that they introduce, with particular attention on numerical and computational aspects. I will present some new algorithmic or technical solutions recently proposed, one of which is the results of a collaboration between chemists and applied mathematicians.