## Kategorie: ‘Videos’

## Charlemagne Distinguished Lecture Series Video 2020 Part 2

Prof. George Em Karniadakis, Ph.D. – Physics-Informed Neural Networks (PINNs): An Alphabet of Algorithms for Diverse Applications

Twice a year, the AICES & IRTG-2379 fellows are in charge of the organization of 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 as well as experience. As the 19th speaker of the Charlemagne Distinguished Lecture Series, the IRTG & AICES fellows invited Prof. George Em Karniadakis, Ph.D.,Professor of Applied Mathematics, of the The Charles Pitts Robinson and John Palmer Barstow, Brown University, USA. Professor Karniadakis gave a talk on „Physics-Informed Neural Networks (PINNs): An Alphabet of Algorithms for Diverse Applications“ on October 19, 2020 at 3:00 pm.

## EU Regional School Videos 2020 Part 2

**Course 6 – Prof. Leszek Demkowicz, Ph.D. – ****The Discontinuous Petrov-Galerkin (DPG) Method (with Optimal Test Functions)**

**Course 8 – Prof. Dr. Felix Krahmer – ****Structure and Randomness in Data Science**

In many applications related to computing, in contrast, randomness plays another essential role: A random preprocessing of the data, such as a random projection or a random subsampling, can allow to reduce high dimensional problems to lower dimensional problems that can be solved more efficiently. In most of these scenarios, comparable deterministic constructions are not feasible, as for any realization of the preprocessing operation, the procedure will fail for some data sets. The role of randomness in this context is that it translates a method that works for most instances – which may be useless as the actual data set may be one of the bad instances – into a method that for any data set works with high probability. Structure is important to ensure that the preprocessing remains efficient and its computational complexity does not exceed the one of the task to be performed. Examples of problems where this strategy is of use include nearest neighbour search and principal component analysis.

In the course, we will give an overview of instances of both these paradigms from various application areas and also present some key ideas of the underlying mathematical theory.

## EU Regional School Videos 2020 Part 1

**Course 1 – Prof. Dr. Siddhartha Mishra – Current Topics in Numerical methods for hyperbolic systems of conservation laws: uncertainty quantification, statistical solutions and machine learning**

This short course is devoted to the topic of *state and parameter estimation* problems where the goal is to compute a fast reconstruction of the state and associated parameters of interest of a physical system from available measurement observations and the knowledge of a physical PDE model. Due to their ill-posedness, these problems are often addressed with Bayesian approaches. However, in view of their high numerical cost, especially in a high dimensional framework, strategies involving reduced models have recently been proposed as a vehicle to reduce complexity. In this talk, we present an overview of this alternative approach and illustrate its potential for several different applications: inverse problems in biological flows, pollutant estimation in urban areas and nuclear engineering applications.

## Charlemagne Distinguished Lecture Series Videos 2020 Part 1

Prof. Anthony T. Patera, Ph.D. – Parametrized Partial Differential Equations: Mathematical Models, Computational Methods, and Applications

Twice a year, the IRTG & 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.

As the 18th speaker, the fellows invited Prof. Anthony T. Patera, Ph.D., Ford Professor of Engineering and Professor of Mechanical Engineering, of the Department of Mechanical Engineering, Massachusetts Institute of Technology, USA. Professor Patera gave a talk on „Parametrized Partial Differential Equations: Mathematical Models, Computational Methods, and Applications“ on January 31, 2020 at 3:00 pm at HKW Aachen.

## EU Regional School Videos 2019 Part 1

**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

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

## EU Regional School Videos 2019 Part 2

**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.