Workshop 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