Dr. Stefan Kollmannsberger - Simulation in Additive Manufacturing with Modern Discretizational Techniques

Chair of Computation in Engineer, Technical University of Munich, Germany

Abstract

The talk will present a general framework for the simulation of (initial) boundary value problems which may be defined on almost any type of geometric model. This framework is the finite cell method, a high order embedded domain method which the presenter has helped to develop in the recent decade.

 

Geometric models which form the basis of computational mechanics often stem from Computer Aided Design (CAD). Two variants dominate in this setting: Constructive Solid Geometry (CSG) and boundary representation (B-Rep). The usual CAD to computational analysis process requires the generation of boundary conforming meshes. These, in turn, require the geometric models to be valid, i.e. water tight and flawless. To the contrary, industrial models are often flawed such that model healing must be carried out before boundary conforming meshes can be generated. This presentation will demonstrate how such a potentially expensive healing step may be avoided and how it is possible to directly compute on geometrically and topologically flawed models.

 

Another type of geometric model are voxel models. They usually stem from computed tomography and are omnipresent, for example in medical applications. Yet, for example for the computation of implants, it is advantageous to augment voxel models by B-Rep models using CSG operations. The talk will discuss how computational analysis is possible on these combined models within the presented framework.

 

For some applications, as for example in the computational analysis of historic structures, neither CAD nor voxel models are available. Moreover, the construction of an accurate, reverse-engineered CAD model is extremely complex and only possible in a very limited number of cases. As a remedy, this talk will present a new paradigm: to use pictures directly as geometric models for computational mechanics.  The talk will close demonstrating the computational analysis of complex-shaped large historic structures from drone images.

Prof. Dr. Torsten Hoefler - MPI Remote Memory Access Programming and Scientific Benchmarking of Parallel Codes

Abstract

We will provide an overview of advanced MPI programming techniques. Specifically, we will focus on MPI-3's new Remote Memory Access (RMA) programming and an implementation thereof. We will discuss how to utilize MPI-3 RMA in modern applications. Furthermore, we will discuss issues in large-scale implementation and deployment. The lecture will then continue to a small number of other advanced MPI usage scenarios that every scientific computing researcher should know. Finally, we will discuss how to benchmark parallel applications in a scientifically rigorous way. This turns out to be surprisingly difficult and the state of the art is suboptimal. We will present twelve simple rules that can be used as guidelines for good scientific practice when it comes to measuring and reporting performance results.

Prof. Dr. Torsten Hoefler - High-Performance Communication in Machine Learning

Abstract

One of the main drivers behind the rapid recent advances in machine learning has been the availability of efficient system support.
Despite existing progress, scaling compute-intensive machine learning workloads to a large number of compute nodes is still a challenging task. In this talk, we provide an overview of communication aspects in deep learning. We address the communication challenge, by proposing SparCML, a general, scalable communication layer for machine learning applications. SparCML is built on the observation that many distributed machine learning algorithms either have naturally sparse communication patterns, or have updates which can be sparsified in a structured way for improved performance, without loss of convergence or accuracy. To exploit this insight, we analyze, design, and implement a set of communication-efficient protocols for sparse input data, in conjunction with efficient machine learning algorithms which can leverage these primitives. Our communication protocols generalize standard collective operations, by allowing processes to contribute sparse input data vectors, of heterogeneous sizes. Our generic communication layer is enriched with additional features, such as support for non-blocking
(asynchronous) operations and support for low-precision data representations. We validate our algorithmic results experimentally on a range of large-scale machine learning applications and target architectures, showing that we can leverage sparsity for order-of-magnitude runtime savings, compared to existing methods and frameworks.

Prof. Dr. Mapundi Banda - A Lyapunov Approach to Boundary Feedback Stabilisation for Hyperbolic Balance Laws: a Numerical Perspective

Department for Mathematics and Applied Mathematics, University of Pretoria, South Africa

Abstract

First-order systems of evolution models governed by time-dependent hyperbolic partial dierential equations will be considered. In this talk we will present a review of the Lyapunov approach for boundary feedback stabilisation for such dierential equations. The rst part of the presentation will give an overview of recent results in the mathematical analysis of stabilisation of hyperbolic balance laws. The second part will then discuss a numerical approach to discretise the balance laws. This will be followed by a numerical analysis for the discrete Lyapunov approach. A selection of examples will be discussed and the eectiveness of the numerical stabilisation will also be demonstrated.

Prof. Dr. Gerhard Holzapfel - Models for Fiber-Reinforced Elastic Solids with a Focus on Soft Biological Tissues

Institute of Biomechanics, Graz University of Technology, Austria

Abstract

This short course provides a summary of models for fiber-reinforced elastic solids with distributed fiber orientations. As a motivation we start with a simple 1D problem which we then develop further to 3D considering a 3D isotropic fiber dispersion, perfectly aligned fibers, a rotationally symmetric dispersion and a non-rotationally symmetric dispersion. We review basic elements from the nonlinear theory of continuum mechanics that is required in the modeling of fiber-reinforced elastic solids. Of particular relevance are the structure tensors and related deformation invariants required to consider fibers and their dispersed directions in constitutive models. We also provide computational aspects needed for finite element implementation of the discussed models, and focus on an efficient formulation which avoids non-physical responses in the numerical analysis of anisotropic materials. The effect of the fiber structure on the material response is discussed on the basis of several examples. We discuss changes of the fiber structure in images obtained from cardiovascular tissues in health and disease using high-resolution optical microscopy. Related finite-element simulations highlight the need to incorporate the structural differences of soft biological (fibrous) tissues.