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DTSTAMP:20260422T000605Z
LOCATION:DEF Concourse
DTSTART;TZID=America/Denver:20231116T100000
DTEND;TZID=America/Denver:20231116T170000
UID:submissions.supercomputing.org_SC23_sess304_rpost105@linklings.com
SUMMARY:HPC Accelerated Generative Deep Learning Approach for Creating Dig
 ital Twins of Climate Models
DESCRIPTION:Johannes Meuer, Christopher Kadow, and Thomas Ludwig (German C
 limate Computing Centre (DKRZ)) and Claudia Timmreck (Max Planck Institute
  for Meteorology)\n\nClimate models cannot perfectly represent the complex
  climate system, but by running them multiple times with small variations 
 in input parameters, it's possible to estimate uncertainties and explore d
 ifferent climate scenarios. Generating these ensembles demands significant
  computational resources and time, which can be crucial for risk assessmen
 ts and decision-making. This study utilizes generative adversarial network
 s (GANs) and deep diffusion models (DDMs) to produce low-resolution ensemb
 le runs trained on data provided by climate model simulations with low com
 putational expense. Additionally, convolutional neural networks (CNNs) are
  employed for downscaling as well as parallelization techniques to enhance
  performance and reduce computation time. This approach allows for time-ef
 ficient exploration of high-resolution ensemble members, facilitating clim
 ate modeling investigations that were previously challenging due to resour
 ce constraints.\n\nTag: Artificial Intelligence/Machine Learning, Architec
 ture and Networks, Heterogeneous Computing, I/O and File Systems, Performa
 nce Measurement, Modeling, and Tools, Post-Moore Computing, Programming Fr
 ameworks and System Software, Quantum Computing\n\nRegistration Category: 
 Tech Program Reg Pass, Exhibits Reg Pass\n\n
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