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Case Studies Scientific Computing (MA4306)

Fig. 1 - Computation of an angle of attack at the tip of a five hohle probe by means of two calibration parameters. Such probes for the determination of angles of attack and velocities can be found e.g. at the front part of airplanes. This problem was part of Project 2: Calibration of multi-hohle probes in the winter term 2019/20.


Lecture Prof. Dr. Callies, Dr. Tobias Köppl
Question time by arrangement (Email)
Supervision Dr. Tobias Köppl, M.Sc. Gladys Gutiérrez
Requirements Basic courses on numerical mathematics:
MA1304 Introduction to Numerical Linear Algebra
MA2304 Numerical Methods for Ordinary Differential Equations
MA3303 Numerical Methods for Partial Differential Equations
Credits 6 ECTS
TUMonline Link zum TUMonline Eintrag
Moodle To get access to the Moodle course, please register in TUMonline for the lecture. Afterwards you are automatically registered for the Moodle course.


Basic Concept

Students participating in this module will work on a practical problem in small groups under the supervision of the lecturers (see Fig. 1). The project work typically starts with the discussion of the problem setup, an analysis of the important problem characteristics and a subsequent formulation as a mathematical model. During this phase, the students also present their challenges to a non-scientific audience, usually in the form of a poster presentation. They discuss their poster ideas with the supervisors and receive peer-feedback on their presentations. The participants then research suitable solution algorithms and receive lectures on additional skills where necessary. They discuss their solution approaches with the project supervisors and refine and implement the chosen algorithms. They assess and discuss their solutions and the practical properties of their algorithm with the supervisors and implement necessary modifications or enhancements and / or contrast the properties of different solution approaches with respect to the underlying application. During the project work the students discuss their progress with their supervisors from mathematics and from the field of application on a regular basis and give intermediate presentations of their problem, its characteristics and their solution approaches to the other participants. At the end, the results are presented in the form of conference talks to a scientific audience.


The final grade is composed of the following subtasks:


Registration for this course is mandatory and has to be done before the deadline: 14th April 2021.
The registration is done by email to providing the following information:

Please note that there is only a limited number of places for this module, since for each project we can only accept up to three students.


Project 1: Machine learning methods for simulating the impact of left ventricular assistant devices (LVAD)

Cardiogenic shock is an illness that is characterized by the disability of the heart to provide enough blood and oxygen for the vital organs. Some of the most common causes for this illness are: heart attack, heart failure, chest injuries or internal bleeding. In order to compensate the lack of blood supply, the heart is equipped with assistance device such as blood pumps. Typically they are installed in the left ventricle, since the left ventricle of the heart pumps blood with high pressure into the systemic part of the cardiovascular circulation. Such devices are called left ventricle assistance devices (LVAD). Using a LVAD in an efficient way, the settings (rotations per minute (rpm), type of the pump etc.) of the LVAD have to be selected carefully. The goal of this project is to design a simple surrogate model mapping these settings to blood pressure and velocity curves reported in different arteries of the systemic circulation. By this it is intended to avoid computationally expensive simulations of blood pressure and velocity curves. This enables the test of different settings in a short time. In order to construct a surrogate model for this purpose, machine learning techniques should be considered and tested. The data for the training phase are provided by the company Computational life.

The subtasks of this project read as follows:

Contact: Computational life, USA, 251 Little Falls Drive, Wilmington, New Castle County. Delaware 19808-1674, Pfeil


Project 2: Neural network based calibration of five hole probes

In order to enable safe operation of ships and planes (see Figure 1), it is necessary to have an accurate knowledge on flows around them. In case of planes, probes are placed at certain distances on their wings. These probes contain several sensors that measure static and dynamic pressures as well as velocities and angles of attack. An accurate measurement of these units is achieved by calibrating each probe before it is used. The necessity for this arises from the fact that the dependence of the measured pressures on the angle of attack varies from probe to probe. This is mainly due to the manufacturing tolerances. The goal of this project is to understand and program calibration algorithms for multi-hole probes.

The subtasks of this project read as follows:

This project is supported by the company Vectoflow GmbH. Vectoflow provides measurements taken in wind tunnels. These measurements should be used to test the MATLAB/C++ programs mentioned above. Furthermore, you are assisted by vectoflow to get a better understanding of the different issues related to the project.

Contact: Vectoflow GmbH, Friedrichshafener Straße 1, 82205 Gilching, Dr. Christian Haigermoser, Pfeil.


Project 3: Optimization of artificial neural networks (ANN)

An ANN is a system of nodes which are referred to as artificial neurons, since they can be compared to neurons in a brain. A connection or edge between two nodes, like the synapses in a biological brain, can send a signal to other nodes. A node receiving a signal can send it to other nodes which are connected to it. Within an ANN the signal is a real number, and the output of each node is provided by a combination of non-linear functions and its inputs. To each node (neuron) and edge one assigns a weight that is adjusted during a training process. According to the weight the strength of the signal at a connection can be strengthened or weakened. Nodes typically have also a threshold such that signals received by the node such that they are only transmitted if they exceed this threshold. Typically, nodes are grouped together in form of layers. The different layers typically connect by edges linking the different nodes of the layers. Signals enter the network at the first layer (input layer) and leave the last layer (output layer) corresponding to quantities of interest. ANNs have successfully been used as simple surrogate models for complex relationships. For a learning process a sufficient amount of training data is required to determine the different weights. However, it is in general not quite clear how many nodes or neurons and layers have to be chosen to obtain an accurate ANN. The goal of this project is to find algorithms and estimates that can help to design an optimal structure for ANNs. In this context, the term optimal indicates that an ANN contains a minimal number of layers and neurons.

The subtasks of this project read as follows:

Contact: Vitesco Technologies GmbH, Siemensstraße 12, 93055 Regensburg, MSc. Michael Wutz, (Vitesco Technologies Pfeil)


Project 4: Optimizing the Quality of Risk Assessments of Biological Epidemics

A description of this project can be found here: Optimizing the Quality of Risk Assessments of Biological Epidemics

-- TobiasKoeppl - 03 Mar 2021