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Digital Network

Machine Learning & Artificial Intelligence
Virtual Workshop

August 2-4, 2023 (9am-11:30am PDT, 12-2:30pm ET-Chile Time) 

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.

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.”

The SZ4D Virtual Workshop on Machine Learning and Artificial Intelligence (ML/AI) will take place August 2-4, 2023, with three sessions focused on different themes. Each session will have three invited speakers (1hr), focused breakouts (1hr), & report back/synthesis (0.5hr). More details about each session and link t register in advance below.


Daniel Trugman (University of Nevada Reno)

Tushar Mittal (Penn State)

Xuesong Ding (UT Austin)

Session 1
August 2, 9am-11:30am PDT
Theme: 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


Confirmed Invited Speakers

  • Weiqiang Zhu (UC Berkeley) - FEC

  • Evan Goldstein (UNC Greensboro) - LS

  • Matthew Head (U. Illinois) - MDE


Session 1 Introduction | Daniel Trugman (University of Nevada Reno)

Before I train the model… : Adventures in developing machine learning systems for coastal geomorphology | Evan Goldstein (UNC Greensboro)

Deep learning for earthquake monitoring | Weiqiang Zhu (UC Berkeley)

Machine Learning and the Mogi model: Improving the efficiency of ensemble-based methods for volcano deformation analyses | Matthew Head (U. Illinois)

Session 2
August 3, 9am-11:30am PDT
Theme: 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


Session 2 Introduction | Tushar Mittal (Penn State)

Differentiable modelling to unify machine learning and physical models for hydrology and geosciences | Chaopeng Shen (Penn State)

Signal extraction and characterization from geodetic datasets using AI approaches | Christelle Wauthier (Penn State)

Early warning for great earthquakes from characterization of crustal deformation patterns with deep learning | Diego Melgar (University of Oregon)

Session 3
August 4, 9am-11:30am PDT
Theme: 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


Session 3 Introduction | Daniel Trugman (University of Nevada Reno)

Rapid 3D Seismic Waveform Modeling using Fourier Neural Operators | Qingkai Kong (Lawrence Livermore National Laboratory)

Geometry-Informed Neural Operator for Large-Scale 3D PDEs | Zongyi Li (Caltech)

Bridging length-time scales in a brittle-ductile process: Evolution of “defects” in fast-slow time space | Hamed O’Ghaffari (MIT)

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