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IRTG Modern Inverse Problems (MIP)

Kategorie: ‘Videos’

EU Regional School Videos 2019 Part 1

24. Juni 2019 | von

Course 1 – Prof. Dr. Torsten Hoefler – MPI Remote Memory Access Programming and Scientific Benchmarking of Parallel Codes

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.

Course 3 – Prof. Alessandro Reali, Ph.D. – Isogeometric Analysis: An Introduction and Some Recent Advances

Isogeometric Analysis (IGA) is a recent simulation framework, originally proposed by T.J.R. Hughes and coworkers in 2005, to bridge the gap between Computational Mechanics and Computer Aided Design (CAD). The basic IGA paradigm consists of adopting the same basis functions used for geometry representations in CAD systems – such as, e.g., Non-Uniform Rational B-Splines (NURBS) – for the approximation of field variables, in an isoparametric fashion. This leads to a cost-saving simplification of the typically expensive mesh generation and refinement processes required by standard finite element analysis. In addition, thanks to the high-regularity properties of its basis functions, IGA has shown a better accuracy per-degree-of-freedom and an enhanced robustness with respect to standard finite elements in a number of applications ranging from solids and structures to fluids, opening also the door to geometrically flexible discretizations of higher-order partial differential equations in primal form, as well as to highly efficient (strong-form) collocation methods.
The first part of this short course is devoted to the introduction of the basic concepts of IGA (including a brief primer on B-Splines and NURBS). The unique potential of IGA is then shown through some convincing applications, mainly belonging to the field of structural mechanics and of fluid-structure interaction, where the superior results that can be provided by IGA with respect to standard finite elements are clearly pointed out. 
The lecture is finally concluded by a brief presentation of further IGA works in progress and new ideas.

Charlemagne Distinguished Lecture Series Videos 2019 Part 1

10. April 2019 | von

Prof. Karen Willcox, Ph.D. – Projection-based Model Reduction: Formulations for Scientific Machine Learning

Twice a year, the AICES fellows organize the prestigious Charlemagne Lecture, whose objective is to invite persons, who have achieved impressive accomplishments throughout their career and, in this sense, to get inspired by their scientific achievements. 
Prof. Karen Willcox, Ph.D., Director of the Oden Institute for Computational Engineering and Sciences, Professor of Aerospace Engineering and Engineering Mechanics at Oden Institute for Computational Engineering and Sciences, the University of Texas at Austin, USA. Professor Willcox gave a talk on „Projection-based Model Reduction: Formulations for Scientific Machine Learning“ on April 30, 2019 at 4:30 pm.

Charlemagne Distinguished Lecture Series Videos

10. April 2019 | von

EU Regional School Videos 2019 Part 2

10. April 2019 | von

Course 5 – 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.

Course 7 – Prof. Dariusz Uciński Ph.D. – Optimum Experimental Design for Distributed Parameter System Identification

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 Videos

10. April 2019 | von

Videos

10. April 2019 | von