Programme
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Tuesday
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| 7 November 2023 | ||
|---|---|---|
| MAELSTROM | ||
| 09:00 to 09:30 |
An introduction to MAELSTROM
Presentation slides |
Peter Düben (ECMWF)
|
| 09:30 to 10:00 |
Performance models for machine learning
Presentation slides |
Karthick Panner Selvam (University of Luxembourg)
|
| 10:00 to 10:30 | Experiences with W&C ML Apps on AMD Instinct GPUs |
Stepan Nassyr (Jülich Supercomputing Center)
|
| 10:30 to 11:00 |
A machine learned weather forecast for Norway
Presentation slides |
Thomas Nipen (Norwegian Meteorological Institute)
|
| 11:00 to 11:15 | Coffee break | |
| Partner EuroHPC Projects | ||
| 11:15 to 11:45 | SEA-Projects: towards a European heterogeneous system and SW architecture for Exascale and beyond |
Hans-Christian Hoppe (Jülich Supercomputing Center)
|
| 11:45 to 12:15 |
SparCity for Sparse Tensors: Study on Feature Extraction and Smart Tensor Generation
Presentation slides |
Tugba Torun (Koç University)
|
| 12:15 to 12:45 |
Some recent improvements of parallel-in-time algorithm
Presentation slides |
Daniel Ruprecht (Hamburg University of Technology)
|
| 12:45 to 14:00 | Lunch break | |
| Invited external speakers | ||
| 14:00 to 14:30 |
Radiative transfer emulation: results so far and why we should move on to 3D
Presentation slides |
Peter Ukkonen (DMI)
|
| 14:30 to 15:00 |
Deep Learning for regional ensemble forecasting : first results
Presentation slides |
Laure Raynaud (Météo-France)
|
| 15:00 to 15:30 |
AIFS
Presentation slides |
Simon Lang (ECMWF)
|
| 15:30 to 16:00 | Coffee break | |
| 16:00 to 16:30 |
AtmoRep: Large-Scale Representation Learning of Atmospheric Dynamics
Presentation slides |
Christian Lessig (ECMWF)
|
| 16:30 to 17:00 |
Physics-Constrained Deep Learning for Downscaling and Emulation
Presentation slides |
Paula Harder (Fraunhofer Institute ITWM)
|
| 17:00 to 17:30 |
Towards km-scale AI emulation for weather and climate applications
Presentation slides |
Karthik Kashinath (NVIDIA)
|
| Drinks reception | ||
| 18:00 to 19:30 | Drinks reception in the Weather Room | |