About
In response to the significant engineering challenges hindering scientific research, CERN and European research institutes are pioneering a standardized digital twin framework to streamline and enhance the efficiency of scientific workflows. This talk will detail the development of advanced machine learning (ML) solutions at CERN, focusing on distributing model training and automating MLOps across high-performance computing (HPC) infrastructures. Key initiatives include integrating distributed deep learning and hyper-parameter optimization tools into traditional ML training workflows for optimal deployment and management. As a summer student, I am involved in applying and validating the ML Digital Twin Framework(itwinai) across diverse digital twin applications, from high-energy physics to environmental monitoring. This presentation will highlight my experiences with leading ML frameworks such as PyTorch, showcasing their impact on accelerating scientific innovation in digital twin technology..
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The talk
Credits and licence
- Institution
- CERN
- Department
- CERN openlab Summer Student Programme 2024
- Speaker
- Henry Mutegeki
- Year
- 2024
- Language
- English
- Subjects
- Computer science
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed