In this course various (astro)physical problems will be solved with diverse computational methods: Fourier-transformation, population synthesis & Markov chain Monte Carlo, N-body simulations, Hydrodynamical/Computational fluid dynamics simulations, High Performance Computing, radiative transfer, advanced visualization techniques.

We review the various computational methods used in (astro)physics, with a problem-oriented approach: we take an astrophysical problem and discuss how to solve that type of problem numerically. We will do data analysis, computer simulations, and visualization approaches that are not only used in astrophysics, but other physical fields, mathematical fields and engineering.


  • advanced linux terminal commands & scripts, e.g. how to use awk as a computing tool, how to manipulate big data with shell scripts
  • astronomical databases and archives to retrieve data for computations & statistics
  • Gnuplot as a visualization and computing tool
  • time series analysis (Discrete Fourier Transformation, power spectrum, box-fitting least square)
  • population synthesis & Markov chain Monte Carlo
  • N-body simulations
  • hydrodynamical/computational fluid dynamics simulations (various methods, mesh refinement)
  • 3D visualization and rendering with Paraview, streamline integration, animations
  • basics of High Performance Computing
  • Radiative Transfer with flux limited diffusion approx, role of opacity, opacity considerations and computations; Radiative transfer with ray-tracing approach (using RADMC-3D)

Prerequisites: basic linux terminal commands, basic programming knowledge in any language.


May 2-6 & May 9-10, 2022 in the afternoon, online via Zoom


Max. 20 participants

Please be aware that the registration opens January 1 with a deadline at the end of the 2nd week of the Spring Semester 2022.

Registration Conditions

  • ETH students can register in myStudies
  • Students from universities that have official cooperation agreements with ETH Zurich may register as special students. No registration fee applies.
  • ETH Domain Employees can register as an auditor. The registration is free of charge for ETH domain employees.
  • Non ETH students may register as auditors following the instructions provided here. A fee of 200 CHF applies for this Block Course. The invoice will be established after the sixth week of the semester. It will be dispatched by e-mail to your ETH e-mail address and must be paid within 30 days

The course presents fundamental concepts and advanced methodologies for handling and interpreting data in relation with models. It elaborates on methods and tools for identifying, quantifying and propagating uncertainty through models of systems with applications in various fields of Engineering and Applied science.

The course is offered as part of the Computational Science Zurich (CSZ) (http://www.zhcs.ch/) graduate program, a joint initiative between ETH Zürich and University of Zürich. This CSZ Block Course aims at providing a graduate level introduction into probabilistic modeling and identification of engineering systems. Along with fundamentals of probabilistic and dynamic system analysis, advanced methods and tools will be introduced for surrogate and reduced order models, sensitivity and failure analysis, parallel processing, uncertainty quantification and propagation, system identification, nonlinear and non-stationary system analysis.

The topics to be covered are in three broad categories, with a detailed outline available online (see Learning Materials).
Track 1: Uncertainty Quantification and Rare Event Estimation in Engineering, offered by the Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich (18 hours)
Lecturers: Prof. Dr. Bruno Sudret, Dr. Stefano Marelli
Track 2: Bayesian Inference and Uncertainty Propagation, offered the by the System Dynamics Laboratory, University of Thessaly, and the Chair of Computational Science, ETH Zurich (18 hours)
Lecturers: Prof. Dr. Costas Papadimitriou, Dr. Georgios Arampatzis, Prof. Dr. Petros Koumoutsakos
Track 3: Data-driven Identification and Simulation of Dynamic Systems, offered the by the Chair of Structural Mechanics, ETH Zurich (18 hours)
Lecturers: Prof. Dr. Eleni Chatzi, Dr. Vasilis Dertimanis
The lectures will be complemented via a comprehensive series of interactive Tutorials.

CSZ Block Course

Updated on 2021-11-09T12:33:46+01:00, by Eleni Chatzi.