[hpc-announce] Reminder: Supercomputing Spotlights: by Dr. Ann Almgren, August 13, 2025

[Erin Carson]

We would like to remind you about this week's Supercomputing Spotlights 
webinar:

Adaptive Mesh Refinement for Multiphysics Applications
Presenter: Dr. Ann Almgren, Lawrence Berkeley National Laboratory
Wednesday, August 13, 2025, 3:00-3:40 pm UTC (30 min talk + 10 min 
questions)
8 am PDT / 10 am CDT / 11 am EDT / 3 pm UTC / 5 pm CEST / 12 am JST (Aug 
14)

Participation is free, but registration is required
Registration link:  
https://urldefense.us/v3/__https://siam.zoom.us/webinar/register/WN_Tgb2dUwqRUeiQ0r7tUriqA__;!!G_uCfscf7eWS!bEVJ27mwKPqmELJGChFdDxUpMKBD1lgjcigvDu852LlimUaI6J4QrosRa0h6AdKWhWwYYW_O05RGzvrXU8sCS2d8BDuNCJE$ 


Supercomputing Spotlights is a webinar series featuring short 
presentations that highlight the impact and successes of 
high-performance computing (HPC) throughout our world. Presentations, 
emphasizing achievements and opportunities in HPC, are intended for the 
broad international community, especially students and newcomers to the 
field. Supercomputing Spotlights is an outreach initiative of 
SIAG/Supercomputing (https://urldefense.us/v3/__https://siag-sc.org__;!!G_uCfscf7eWS!bEVJ27mwKPqmELJGChFdDxUpMKBD1lgjcigvDu852LlimUaI6J4QrosRa0h6AdKWhWwYYW_O05RGzvrXU8sCS2d8KNszVcA$ ) …  Join us!

Abstract: Adaptive mesh refinement (AMR) is one of several techniques 
for dynamically modifying the spatial resolution of a simulation in 
particular regions of the spatial domain. Block-structured AMR 
specifically refines the mesh by defining locally structured regions 
with finer spatial, and possibly temporal, resolution. This combination 
of locally structured meshes within an irregular global hierarchy is in 
some sense the best of both worlds in that it enables regular local data 
access while enabling greater flexibility in the overall computation. 
AMR has come a long way since it was first developed.  In this talk I 
will give a short overview of block-structured AMR for different types 
of applications and will discuss how it has become both more powerful 
and more complicated, and how open-source software is enabling 
non-experts to take advantage of this important technique.

Bio: Ann Almgren is a senior scientist in the Applied Mathematics and 
Computational Research Division of Lawrence Berkeley National Laboratory 
and the Department Head of Berkeley Lab's Applied Mathematics 
Department. Her primary research interest is in computational algorithms 
for solving partial differential equations in a variety of application 
areas. Her current projects include the development and implementation 
of new multiphysics algorithms in high-resolution adaptive mesh codes 
that are designed for the latest hybrid architectures. She is a SIAM 
Fellow, serves on the editorial boards of CAMCoS, IJHPCA and Phil. 
Trans. A., and co-leads LBL's Computing Sciences Area Mentoring Program. 
In 2023 she was awarded the Berkeley Lab Director's Award for 
Exceptional Scientific Achievement. Prior to coming to LBL she worked at 
the Institute for Advanced Study in Princeton, NJ, and at Lawrence 
Livermore National Lab.


Best regards,
The SIAG/SC officers for 2024-2025
Ulrike Meier Yang (chair)
Rio Yokota (vice chair)
Hartwig Anzt (program director)
Erin Carson (secretary)