Date and Place: Thursdays and hybrid (live in 32349/online via Zoom). For detailed dates see below!
Content
In the Scientific Computing Seminar we host talks of guests and members of the SciComp team as well as students of mathematics, computer science and engineering. Everybody interested in the topics is welcome.
List of Talks

Thu24Oct2019
11:30SC Seminar Room 32349
Mehmet Akif Cördük, TU Kaiserslautern
Title:
GPU Based Price EnginesAbstract:
High performance computing is needed in many areas of the industry to reduce the cost, energy and increase performance of certain tasks. Thanks to the advancements in GPGPU computing, it is more accessible for everyone to develop high performance solutions for parallel tasks. In this work, we present a novel solution for online computation of hotel prices and combinations with other services. High cardinality of search space is a time consuming task for CPU based solutions and cache based solutions are not always uptodate. We map the problem to GPU architecture considering hardware limitations and memory access considerations. The proposed solution provides high cardinality , flexible searches with accurate and uptodate results, thanks to highly parallel nature of the problem. We achieved a 45 requests/seconds throughput for a search interval of 30 days for 88000 hotel rooms by computing 120 million prices per second.

Thu07Nov2019
11:30SC Seminar Room 32349
Jonas Kusch, Karlsruhe Institute of Technology
Title:
A oneshot approach for the Intrusive Polynomial Moment methodAbstract:
Uncertainty Quantification for fluid dynamics applications becomes a challenging task, especially when the solution shows shocks in the random space. To enable the use of adaptivity, we use intrusive methods, which provide a set of equations describing the time evolution of the polynomial chaos (PC) coefficients. The Intrusive Polynomial Moment (IPM) method possesses a large number of desirable quantities that are violated by standard intrusive methods such as stochasticGalerkin. However, the IPM system requires solving a convex optimization problem which computes the entropy variables corresponding to a given set of PC coefficients. This optimization problem needs to be solved in every spatial cell in each time step, yielding high computational costs.
In this talk, we present a method to reduce computational costs for steady state problems. To drive the IPM system to its steady state solution, we use explicit Euler steps, while employing Newton’s method to solve the mentioned optimization problems. Since the PC coefficients are not at the physically correct steady state solution for a large number of Euler steps, we propose to not solve the IPM optimization problems exactly. Instead, we perform only one step of Newton’s method, i.e. we converge the PC coefficients as well as the corresponding entropy variables simultaneously to their steady state solution. It can be shown that this method converges to the steady state solution locally. Due to its similarities to the oneshot method in shape optimization, we call this method oneshot IPM.
We demonstrate the effectiveness of oneshot IPM by investigating an inviscid flow around a NACA0012 profile with an uncertain Mach number and far field pressure. When making use of the oneshot approach as well as adaptivity, we are able to compete with nonintrusive collocation methods.

Thu14Nov2019
11:30SC Seminar Room 32349
Halil Kaya, Turkish Aerospace Industry
Title:
Generation of surrogatebased aerodynamic model using an adaptive coKriging methodAbstract:
This work presents a multifidelity aerodynamic modeling approach for the aerodynamic model generation of a UAV configuration. The aerodynamic data were generated with CFD simulations. A large number of cheap but less accurate, hence lower fidelity, Euler simulations are coupled with a smaller number of expensive but more accurate, hence higher fidelity, RANS simulations. Although the current study is limited to onedimensional data, findings of the study suggest that the demonstrated method has the potential of being useful in obtaining highresolution multidimensional aerodynamic models for modern aircraft with reasonable effort.

Thu21Nov2019
11:30SC Seminar Room 32349
Ole Burghardt, SciComp
Title:
Multiphysics aspects of simulation and design with SU2Abstract:
In this talk we will discuss multiphysics aspects of simulation and design with the opensource solver suite SU2. An emphasis is put the development of discrete adjoint solvers that is done at SciComp. To showcase its way of application for one of the most important coupled problems in engineering nowadays, we will set up a conjugate heat transfer problem and demonstrate how accurate gradients for finding optimal shapes can be obtained.

Thu28Nov2019
11:30SC Seminar Room 32349
Tim Albring, SciComp
Title:
Automating Continuous Integration and Deployment using Github Actions and DockerAbstract:
Continuous integration – the practice of frequently integrating one’s new or changed code with the existing code repository – should occur frequently enough such that no errors can arise without developers noticing them and correcting them immediately. Rather than periodically scheduled builds, also called ‘nightly builds’, nowadays it is normal practice to trigger these builds by every commit to a repository. While it is obvious so see the benefits for big, professional software projects, even smaller projects on a working group level at a university might benefit from automating integration, building, testing and, possibly also the deployment. In this talk we will go through the individual steps of creating such a CI/CD pipeline starting from using the container platform Docker to the recently introduced GitHub Actions feature.

Thu16Jan2020
11:30SC Seminar Room 32349
Maxime Krier, TU Kaiserslautern
Title:
Bridging Neural Network Architectures and Numerical Differential EquationsAbstract:
Over the past few years, neural networks have become one of the most important tools for supervised machine learning. A class of architectures called ResNets have shown good results on tasks like image classification. We show that ResNet architectures can be interpreted as a numerical discretization of ordinary differential equations. A common problem that arises in training neural networks is the poor generalization to new data. We approach this problem by applying the well examined concept of stability of ODEs to ResNets. In this talk we discuss the construction of a network architecture based on a numerical scheme for solving ODEs. Then we analyze how stability properties of the numerical scheme can be used to deduce characterizations of robustness of the neural network. Based on our results we propose improvements to ResNet architectures that ensure better generalization ability of the network with respect to small perturbations in the data.

Tue21Jan2020
10:00SC Seminar Room 32349
Xinyu Hui, Northwestern Polytechnical University Xi’an, China
Title:
Fast pressure distribution prediction and unsteady periodic flow field prediction method based on deep learningAbstract:
In the aerodynamic design, optimization of the pressure distribution of airfoils is crucial for the aerodynamic components. Conventionally, the pressure distribution is solved by computational ﬂuid dynamics, which is a timeconsuming task. Surrogate modeling can leverage such expense to some extent, but it needs careful shape parameterization schemes for airfoils. As an alternative, deep learning approximates inputsoutputs mapping without solving the eﬃciencyexpensive physical equations and avoids the limitations of particular parameterization methods. Therefore, I present a datadriven approach for predicting the pressure distribution over airfoils based on Convolutional Neural Network (CNN). Given the airfoil geometry, a supervised learning problem is presented for predicting aerodynamic performance. Furthermore, we utilize a universal and ﬂexible parametrization method called Signed Distance Function to improve the performances of CNN. Given the unseen airfoils from the validation dataset to the trained model, the model achieves predicting the pressure coefficient in seconds, with a less than 2% mean square error.
A method based on deep learning to predict periodic unsteady flow field is proposed, and can predict the realtime complex vortex flow state at different moments accurately. Combining conditional generative adversarial network and convolutional neural network, improve the conditional constraint method from conditional generative adversarial network, a deep learning framework with conditional constraints is proposed, which is the regression generative adversarial network. The two scenarios of conditional generative adversarial network and regression generative adversarial network are tested and compared via giving different periodic moments to predict the corresponding flow field variables at this moment. The final results demonstrate that regression generative adversarial network can estimate complex flow fields, and way more faster than CFD simulation.

Thu23Jan2020
11:30SC Seminar Room 32349
Prof. Dr. Hermann G. Matthies, Institut für Wissenschaftliches Rechnen, TU Braunschweig
Title:
Probability, Algebra, Analysis, and NumericsAbstract:
Probability theory was axiomatically built on the concept of measure by A. Kolmogorov in the early 1930s, using the probability measure and the sigmaalgebra as primary objects, and random variables, i.e. measurable functions, and their expectations as secondary. Not long after Kolmogorov ́s work, developments in operator algebras connected to quantum theory in the early 1940s lead to similar results in an approach where algebras of random variables and the expectation functional are the primary objects. Historically this picks up the view implicitly contained in the early probabilistic theory of the Bernoullis.
This algebraic approach allows extensions to more complicated concepts like noncommuting random variables and infinite dimensional function spaces, as it occurs e.g. in quantum field theory, random matrices, and tensorvalued random fields. It not only fully recovers the measuretheoretic approach, but can extend it considerably. For much practical and numerical work, which is often primarily concerned with random variables, expectations, and conditioning, it offers an independent theoretical underpinning. In short words, it is “probability without measure theory”.
This functional analytic setting has also strong connections to the spectral theory of linear operators, where analogies to integration are apparent if they are looked for. This then allows to compute other functions than just polynomials of the algebraic random variables. These links extend in a twofold way to the concept of weak distribution, which describes probability on infinite dimensional vector spaces. Here the random elements are represented by linear mappings, and factorisations of linear maps are intimately connected with representations and tensor products, as they appear in numerical approximations.
Taking this conceptual basis of vector spaces, algebras, linear functionals, and operators gives a fresh view on the concepts of expectation and conditioning, as it occurs in applications of Bayes’ theorem.
For numerical computations, this allows to represent random quantities not only as usual as samples, but as functions of an algebra of known random variables. These may be seen as functions on very highdimensional domains. As already mentioned, tensor approximations appear naturally, and lowrank factorisations are one way to handle the resulting complexity. The resulting connections to an algebra may for example be used in the numerical processing of compressed representations of such highdimensional objects.

Thu06Feb2020
11:30SC Seminar Room 32349
Subhashri Manohar, University of Bozen, Bolzano, Italy.
Title:
A Comparison of Gradient Descent and Stochastic Gradient Descent AlgorithmsAbstract:
In the current decade of Machine Learning, Neural networks are the most sought after method for its ability to solve complex problems with high accuracy. Now, the accurate results is mainly a result of the optimization algorithm contributing to reduce the errors. There are numerous optimization strategies used to produce slightly better and faster results. In this presentation we are going to discuss about the most commonly used algorithms: Standard Gradient Descent and Stochastic Gradient Descent. They are executed on Convolutional Neural Network using MNIST dataset. The results are evaluated on how well the algorithms generalize, accuracy, convergence time and error. Based on that we discuss their advantages and applications.

Thu13Feb2020
11:30SC Seminar Room 32349
Pedro Gomes, Department of Aeronautics, Imperial College London
Title:
Aerodynamic Driven Multidisciplinary Topology Optimization of Compliant AirfoilsAbstract:
We propose a strategy for densitybased topology optimization of fluid structure interaction problems that deals with some shortcomings associated with non stiffnessbased design problems.
The method is demonstrated on a problem where we seek to improve the passive load alleviation characteristics of a highly compliant airfoil at high speed while producing sufficient lift at a lower speed.
The fluid structure interaction is simulated with the multiphysics suite SU2, which includes RANS modelling of the fluid and hyperelastic material behaviour of the geometricallynonlinear structure. Gradientbased optimization is used with the coupled aerostructural sensitivities being obtained via an algorithmic differentiationbased coupled discrete adjoint solver. 
Wed19Feb2020
10:00SC Seminar Room 32349
Max Aehle, TU Kaiserslautern
Title:
Introduction to Model Predictive ControlAbstract:
Model Predictive Control (MPC) is a methodology for controller design. Assuming that the controlled system P (plant) is adequately approximated by another system M (model), MPC iteratively determines its control action by the following strategy: Compute a control sequence that is optimal for M over a certain prediction horizon, and send only the first part of it as actual control input to P. Iterate this.
This method has been employed in a paper by AleksićRoeßner, King et al (2014) to reduce vortex shedding behind a cylinder. We will review the authors’ derivation of the reducedorder model M approximating P, and present an introduction to different control methodologies culminating in MPC. Finally, a plan for (another) computational validation of their affirmative result is laid out.