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Virtual Workshop

Machine Learning and Artificial Intelligence Virtual Workshop

August 2-4, 2023

9am-11:30am PDT, 12-2:30pm ET-Chile Time

Online

registration closed

Objectives

To get community input, buy-in, and feedback regarding what AI/ML approaches are most promising, and what specific steps are needed, to achieve SZ4D goals: 1) Connect useful techniques across domains to enable multi-disciplinary science, 2) Determine and prioritize focus areas for future proposals; and 3) Understand community needs for ML/AI training and community software.

The SZ4D Virtual Workshop on Machine Learning and Artificial Intelligence (ML/AI) took place from August 2-4, 2023, with three sessions focused on different themes. Each session featured three invited speakers, focused breakouts, and a report back/synthesis. More details about each session are provided below.


Motivation from the SZ4D Implementation Plan
 

Given that the initial planning phase of the MCS RCN was focused primarily on physics-based modeling, it will be important to carry out similar efforts for data-driven computational science applications like ML in the next phase of SZ4D  Workshops and other community-building activities are needed to identify community needs, opportunities for open-source software development, and training and educational activities.

Organizers

Daniel Trugman (University of Nevada Reno)

Tushar Mittal (Penn State)

Xuesong Ding (UT Austin)

Agenda

Session 1 | Making Sense of Data with ML/AI
 

Topics and Focus Areas

  • Detection of hidden patterns in geoscience data

  • Dimensionality reduction and clustering techniques

  • Methods enabling analysis and interpretation of large datasets

  • Denoising of time series

  • Transient and anomaly detection

  • Edge computing

 

Invited Speakers

  • Weiqiang Zhu (UC Berkeley) - FEC

  • Evan Goldstein (UNC Greensboro) - LS

  • Matthew Head (U. Illinois) - MDE

Presentations


Session 2 | Making Predictions with ML/AI
 

Topics and Focus Areas

  • Forecasting / time series datasets

  • Data assimilation

  • Causality and causal inference

  • Transfer learning across domains/regions

  • Generative models

  • Optimal experimental design

 

Confirmed Invited Speakers

  • Christelle Wauthier (Penn State) - MDE

  • Diego Melgar (University of Oregon) - FEC

  • Chaopeng Shen (Penn State) - LS


Presentations


Session 3 | Facilitating Process-based Modeling with ML/AI
 

Topics and Focus Areas

  • Accelerating simulations/calculations with ML/AI

  • Reduced order models with ML/AI

  • Physics-informed ML

  • Bridging scales in space and time

  • Data-driven process or subgrid representation

  • Interpretable AI for geoscience

 

Confirmed Invited Speakers

  • Zongyi Li (Caltech) - LS/MDE/FEC

  • Qingkai Kong (LLNL) - FEC

  • Hamed O’Ghaffari (MIT)  - MDE/FEC


Presentations



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