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Fast Simulation with Generative Adversarial Networks

In this video from the Intel User Forum at SC18, Dr. Sofia Vallecorsa from CERN openlab presents: Fast Simulation with Generative Adversarial Networks.

This talk presents an approach based on generative adversarial networks (GANs) to train them over multiple nodes using TensorFlow deep learning framework with Uber Engineering Horovod communication library. Preliminary results on scaling of training time demonstrate how HPC centers could be used to globally optimize AI-based models to meet a growing community need.”

An award-winning effort at CERN has demonstrated potential to significantly change how the physics based modeling and simulation communities view machine learning. The CERN team demonstrated that AI-based models have the potential to act as orders-of-magnitude-faster replacements for computationally expensive tasks in simulation, while maintaining a remarkable level of accuracy.

Dr. Sofia Vallecorsa points out that the CPU based runtime is important as nearly all of the Geant user base runs on CPUs.

As scientists consider future CERN experiments, Vallecorsa observes, “Given future plans to upgrade CERN’s Large Hadron Collider, dramatically increasing particle collision rates, frameworks like this have the potential to play an important role in ensuring data rates remain manageable.”

This kind of approach could help to realize similar orders-of-magnitude-faster speedups for computationally expensive simulation tasks used in a range of fields. Vallecorsa explains that the data distributions coming from the trained machine-learning model are remarkably close to the real and simulated data.

CERN openlab is a unique public-private partnership that accelerates the development of cutting-edge solutions for the worldwide LHC community and wider scientific research. Through CERN openlab, CERN collaborates with leading ICT companies and research institutes.

In this video from SC18 in Dallas, Dr. Sofia Vallecorsa from CERN OpenLab describes how Ai is being used in design of experiments for the Large Hadron Collider.

See our complete coverage of SC18 in Dallas

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