AI and Visualizing large dataset workshop

Name: AI and Visualizing large dataset workshop
Start: 2024-06-11T09:00:00+01:00
End: 2024-06-13T17:00:00+01:00
Location: Princeton University Mader Hall Campus

11 Jun 2024, 09:00 → 13 Jun 2024, 17:00 Europe/Dublin

Room 103 (Princeton University Mader Hall Campus)

Room 103

Princeton University Mader Hall Campus

92 Olden Street, Princeton NJ 08540

Description

The workshop takes place at the Princeton University Campus, Mader Hall.

The workshop aims at bringing together the fusion community to discuss the challenges brought by AI and visualizing large datasets in fusion experiment and simulation.

The three day in hybrid mode event focuses on feedback, lessons learnt and innovative techniques that developers and users experience with AI techniques and visualizing large datasets. A summary of the workshop with all contributors for a journal is one of the key outcomes.

The workshop takes place at the Princeton University Campus, Mader Hall.

Deadline for abstract submission : 24th May 2024.

Main topics:

Topic 1 :Smart indexing of dormant data: How to visualize large signals
- Retrieving all the points of the signal is impossible. Decimation, downsampling techniques are cruciaL
- Place of offline processing : client side versus server side, how to properly downsample a signal [ traditional min/max/avg may not be enough]
  - How to handle dynamic processing of large signals?
  - Is real-time processing of the raw signal the direction?
  - Place of AI in downsampling techniques and integrated data analysis
Topic 2: the use of AI as part of operations
- how to trust the AI workflow?
- how to validate and verify the workflow?
- place of security?
Topic 3 the use of cloud systems in fusion. More and more facilities are using cloud in their environment, either to store and/or compute data
- How cloud is impacting data visualization? Shall everything run in the cloud? Web ?
- Computing power ?
- Open data?
Topic 3: the place of open source utilities: there are very few open sources data visualization tools in fusion?
- Maybe we need a common github entry point where one can find all the open sources related to
- Language? Python, R?

Registration

Registration form - registration form

Tuesday, 11 June
- 1
  
  Welcome message
  
  Speaker: Mr Didier Mazon (CEA)
- 2
  
  Supercomputing in the cloud to accelerate research
  
  Speaker: Ms Roberta Piscitelli (AWS)
  
  AWS for ITER-CEA-PPPL_compressed.pdf
- 3
  
  Machine Learning for prognostics and optimization of particle accelerators
  
  Speaker: Mr Kishan Rajput (Jefferson Lab)
  
  ML4PC.pdf
- 4
  
  Coffee break
- 5
  
  Automatic location of relevant time slices and patterns in both signals and video-movies: real-time and off-line visualization
  
  Speaker: Mr Jesus Vega (CIEMAT)
  
  JVega.ppt
- 6
  
  Advancing Visualisations for Fusion Research at UKAEA
  
  Speaker: Mr Nitesh Bhatia (UKAEA)
  
  Bhatia_Advancing_Visualisations_PPPL.pdf
- 7
  
  Lunch
- 8
  
  AI and Omniverse @NVIDIA
  
  Speaker: Mr Tom Gibbs (Nvidia)
  
  Digital Twin PPPL Workshop 0624.pptx
  
  Digital Twin PPPL Workshop 0624.pptx
- 9
  
  Round Table 1: Smart Indexing of dormant data
- 10
  
  Coffee break
- 11
  
  Round table 2 Fusion and digital twins
- 12
  
  Round Table 3 : Sustainability impact on development
Wednesday, 12 June
- 13
  
  Real-Time Control of CAFe II Using Reinforcement Learning
  
  Speaker: Mr Xiaolong Chen (IMP)
  
  From Sim2Realv1.pptx
- 14
  
  Build, Monitor and Run Digital Twins with Scientific Visualization Tools like ParaView
  
  Speaker: Mr Francois Mazen (Kitware)
  
  2024-06-12 - ITER-Kitware - Digital Twin with ParaView.pdf
- 15
  
  Coffee break
- 16
  
  Data Visualization at ITER
  
  Speaker: Ms Lana Abadie (ITER)
  
  ITERDataVis_talk.pptx
- 17
  
  Smart decimation method applied to real-time monitoring
  
  Speaker: Mr Rodrigo Castro (CIEMAT)
  
  Presentation_Smart_Decimation_Real_Time_Monitoring.pdf
- 18
  
  Lunch
- 19
  
  Wrap-up session
- 20
  
  PPPL tour
Thursday, 13 June
- 21
  
  LHD Data Management
  
  Speaker: Mr Masahiko Emoto (NIFS)
  
  LHD physical Data management (M.Emoto, Data Vis 2024).pptx
- 22
  
  Enhanced Quench Detection at the EuXFEL Through a Machine Learning-Powered Approach
  
  Speaker: Ms Lynda Boukela (DESY)
  
  princetonUniv_LyndaBoukela.pptx
- 23
  
  Trusting Artificial Intelligence in Safety Critical Systems - towards a comprehensive verification framework
  
  Speaker: Ms Joanna Weng (Zurich University of Applied Science)
  
  ITER_JoannaWeng_.pdf
- 24
  
  Coffee break
- 25
  
  Development of Virtual KSTAR: Scientific Visualization and Beyond for Fusion
  
  Speaker: Mr Jae-Min Kwon (Korean Institute For Energy)
  
  (2024-06-13, AI and Vis) V-KSTAR (Kwon, KFE).pdf
- 26
  
  Accelerating Discoveries at DIII-D With the Integrated Research Infrastructure
  
  Speaker: Mr Torrin Bechtel (DIIID)
  
  Viz_Workshop.pdf
- 27
  
  Lunch
- 28
  
  Visualization Techniques for the Gyrokinetic Tokamak Simulation Code
  
  Speaker: Mr Eliot Feibush (PPPL)
  
  FeibushLinks.pdf
- 29
  
  Future perspectives on use of AI and HPC in control room analysis for fusion
  
  Speaker: Mr Michael Churchill (PPPL)
  
  ai_viz_fusion_workshop_20240613.pptx (1).pdf
- 30
  
  Round Table 4 Open data in the cloud AI as part of operations
- 31
  
  Coffee break
- 32
  
  Workshop wrap-up session