C. Scholz: „Workshop: Exploring complex pattern formation with convolutional neural networks”
Heinrich-Heine-Universität Düsseldorf - Seminar@HHUD: 10.11.21 14:00 s.t., Seminarroom 25.33 O0.61

Neural networks have become essential tools for many applications in data science. This has also led to novel perspectives on existing problems in physics. In this workshop we discuss how convolutional neural networks can be used to classify patterns in non-linear dynamical systems. As an example, we consider the Gray-Scott model which is well known for its diversity of different dynamic patterns.[1] Due to the popularity of machine learning in many scientific fields, well maintained open source toolkits are available that make it easy to implement the meth-ods we discuss.

Prerequisites (recommend expertise in two of the following fields): Pattern formation, mathematical optimization, programming (e.g. python) Optional: Bring a laptop to follow the code examples [2]

References

1. C. Scholz and S. Scholz, Exploring complex pattern formation with convolutional neural networks, American Journal of Physics (2021) (to be published)
2. https://github.com/coscholz1984/GS_CNN

C. Scholz: „Workshop: Exploring complex pattern formation with convolutional neural networks”
Heinrich-Heine-Universität Düsseldorf - Seminar@HHUD: 10.11.21 14:00 s.t., Seminarroom 25.33 O0.61

Neural networks have become essential tools for many applications in data science. This has also led to novel perspectives on existing problems in physics. In this workshop we discuss how convolutional neural networks can be used to classify patterns in non-linear dynamical systems. As an example, we consider the Gray-Scott model which is well known for its diversity of different dynamic patterns.[1] Due to the popularity of machine learning in many scientific fields, well maintained open source toolkits are available that make it easy to implement the meth-ods we discuss.

Prerequisites (recommend expertise in two of the following fields): Pattern formation, mathematical optimization, programming (e.g. python) Optional: Bring a laptop to follow the code examples [2]

References

1. C. Scholz and S. Scholz, Exploring complex pattern formation with convolutional neural networks, American Journal of Physics (2021) (to be published)
2. https://github.com/coscholz1984/GS_CNN

Jana Lasser: „Assessing the impact of SARS-CoV-2 prevention measures in schools by means of agent-based simulations calibrated to cluster tracing data”
Graz University of Technology and Complexity Science Hub, Vienna, Austria - Seminar@HHUD: 12.4.21 14:30 s.t., Seminarroom Online

How to safely maintain schools open during a pandemic is still controversial. We aim to identify those measures that effectively control the spread of SARS-CoV-2 in schools. By control we mean that each index case infects less than one other person on average.

Here, we analyze Austrian data on 616 clusters involving 2,822 student-cases and 676 teacher-cases with the aim to calibrate an agent-based epidemiological model in terms of cluster size and transmission risk depending on age and clinical presentation. With this model, we quantify the impact of preventive measures such as room ventilation, reduction of class size, wearing of masks during lessons, and school entry testing by SARS-CoV2-antigen tests.

We find that 40% of all clusters involved no more than two cases, and 3% of the clusters only had more than 20 cases. The younger the students, the more likely we found asymptomatic cases and teachers as the source case of the in-school transmissions. Different school types require different combinations of measures to achieve control of the infection spreading: In primary schools, it is necessary to combine at least two of the aforementioned measures. In secondary schools, where contact networks of students and teachers become increasingly large and dense, a combination of three measures is needed.

A sensitivity analysis indicated that the cluster size might increase up to three-fold in secondary schools for virus variants with an increased transmissibility by 50%, and that poorly executed or enforced mitigation measures might increase the cluster size by a factor of more than 30.

Our results suggest that school-type-specific combinations of measures, when strictly adhered to, allow for a controlled opening of schools even under sustained community transmission of SARS-CoV-2. However, large clusters might still occur on an infrequent, however, regular basis. It is shown explicitly that strict adherence to the measures is a necessary condition for successful control.

Michael Klatt: „Geometrical and Physical Properties of Designer Materials with Novel Types of Short- and Long-Range Order”
Universität Erlangen-Nürnberg / Universität Saarbrücken - Seminar@HHUD: 8.4.21 14:00 s.t., Seminarroom online

Today, material science offers an unprecedented control of exquisite details in microstructured materials. At the same time, modern research in theoretical physics and mathematics continues to find and understand novel types of short- and long-range order. Unleashing the creativity of these theoretical models, we can turn them into microstructured materials with extraordinary geometrical and physical properties and obtain new functionalities. In this talk, we will discuss three examples of complex spatial structures with remarkable physical properties: (1) a hidden long-range order known as 'hyperuniformity'; (2) network structures with photonic band gaps, and (3) nanorough surfaces that reduce the adhesion forces of bacteria.

Dr. Selym Villalba-Chávez: „Probing quantum vacuum-like scenarios with high-intensity laser pulses”
Institut für Theoretische Physik I, Heinrich-Heine-Universität Düsseldorf - Seminar@HHUD: 9.7.2020 16:30 s.t., Seminarroom Online

Gaining comprehensive insights into the properties of the quantum vacuum is central in our continuous quest for obtaining an in-depth and clear understanding of the fundamental laws of our Universe. While the vacuum in classical physics simply describes a region devoid of matter, the framework of quantum field theory reveals that it is characterized by fluctuations of all plausible and realizable fields in Nature. The talk focuses first on the hitherto unobserved effect of the vacuum decay into electron-positron pairs, paying particular attention to a novel way of amplifying this phenomenon. It will be shown that the principle on which this enhancement relies can be tested via gapped graphene monolayers, and revealed that – although this material resembles the QED vacuum – in some processes its two dimensional structure causes some striking differences between both scenarios. The final part of the talk is oriented to show how experiments involving high-intensity lasers can become a powerful tool to limit the parameter spaces of some dark matter candidates such as axions, hidden-photons and minicharged particles.

The left panel depicts the electron/positron spectrum produced by superimposing a strong time dependent electric field and a fast oscillating wave. A scheme for testing the Breit-Wheeler-like production of massive Dirac pairs in a gapped graphene monolayer is shown in the right panel.