After successful completion of the course, students are able to...
In this seminar, we will explore cutting-edge research topics at the intersection of computer science, machine learning and physics. Together we will discuss and analyze papers with the aim to build an understanding beyond the level of mere applications of some currently emerging technologies. For this seminar there is no strict syllabus; the following is a list of possible interesting topics.
The following topics are subject of this course:
Scientific Machine Learning Although it seems that DNNs can be tailored to describe almost any system given enough training data, naive approaches typically neglect prior knowledge about the inner workings of the modeled system. Existing domain knowledge (e.g., physical laws describing the time evolution of a dynamical system) can, however, be used to constrain the admissible solution space, leading to a decrease in required training data and to increased predictive accuracy [4]. By using DNNs in combination with classical numerical algorithms scientific-machine-learning techniques can for example help to solve problems in demand planning, drug discovery and pandemic modelling [5].
The following methods are applied for this course:
If you have any questions, please contact care4u@inso.tuwien.ac.at.
The evaluation results from the active participation by giving a presentation about a chosen topic/publication.
Background knowledge in higher mathematics (e.g., linear algebra, probability theory, basic calculus) will be required.