Scientific Machine Learning (SciML) is a growing field in which methods and techniques from machine learning and scientific computing coalesce. SciML spans across the scientific domains of the CDS, and it is the goal of the SciML Lab to bring together CDS scientists to share their expertise, collaborate on new projects, and foster the research on scientific machine learning.
The name Scientific Machine Learning was coined in January 2018 at a US Department of Energy (DOE) Basic Research Needs workshop; see www.osti.gov/biblio/1478744.
Selection of courses offered in WS 2024/2025
[Lecture] Scientific Machine Learning (Dr. J. Weber)
[Seminar] Adversarial Machine Learning (Prof. Dr. A. Bojchevski)
[Seminar] Machine Learning methods to predict individual differences with Python (Jun.-Prof. Dr. B. Gagl)
[Seminar] Methods of Mathematical Modeling in Life Sciences (PD Dr. T. Mrziglod)
[Seminar] Theory of Machine Learning (Dr. A. Munteanu)
[Seminar] Programming Competition: Modeling of Climate Change Scenarios (Dr. Z. Nikolić)
[Seminar] Selected Topics in Convex Optimization (Prof. Dr. F. Vallentin)
[Seminar] Seminar for Teachers at Grammar and Comprehensive Schools: Practical Algorithms for Instruction (Dr. R. Wienands & Prof. Dr. U. Trottenberg)
[Seminar] Inference and Machine Learning for Scientific Research (Prof. Dr. D. Witthaut)