16-20 September 2019
University of Stuttgart, Campus Vaihingen
Europe/Berlin timezone

Computational Astrophysics

 

Time:
Thursday, September 19, 14:00-18:30

Room: 9.11

Organizers:
Philipp Grete (Michigan State University)

Numerical simulations are a key pillar of modern research. This is especially true for astrophysics where the availability of detailed spatial and temporal data from observations is often sparse for many systems of interest. In many areas large-scale simulations are required, e.g., in support of the interpretation of observations, for theoretical modeling, or in the planning of experiments and observation campaigns. The need and and relevance of large-scale simulations in astrophysics is reflected in a significant share of 25-30% of the overall German supercomputing time. While the supercomputing landscape has been stable for a long time, it started to change in recent years on the path towards the first exascale supercomputer. New technologies such as GPUs for general purpose computing, ARM based platforms (versus x86 platforms), and manycore systems in general have been introduced and require to rethink and revisit traditional algorithms and methods.

This splinter meeting will bring together experts in computational astrophysics from all fields covering (but not limited to) fluid-based methods (from hydrodynamics to general relativistic magnetohydrodynamics), kinetic simulations, radiation transport, chemistry, and N-body dynamics applied to astrophysical systems on all scales, e.g., supernovae, planetary and solar dynamos, accretion disks, interstellar, circumgalactic, and intracluster media, or cosmological simulations.

The goal of this meeting is to present and discuss recent developments in computational astrophysics and their application to current problems.
Thus, contributions involving large-scale simulations and new methods/algorithms are specifically welcome.
In addition to astrophysical results obtained from simulations, speakers are also encouraged to highlight numerical challenges they encountered and how they addressed those in their codes. These may include, but are not limited to, new algorithms (e.g., higher-order methods), changing HPC environments (e.g., manycore, GPUs, or FPGAs), or data storage (e.g., availability of space, sharing, or long term retention).

Agenda:

Related posters:

Name Title
person 1 Title 1
person 2 Title 2
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