Fwd: [HydroML] HydroML 2025
Chaopeng Shen
From: Jasper Alexander Vrugt <jas...@uci.edu>
Date: Wed, Feb 5, 2025 at 5:07 PM
Subject: [HydroML] HydroML 2025
To: hydrom...@hydroml.org <hydrom...@hydroml.org>
Cc: Constance May <con...@uci.edu>, Lawson, Kathryn <ke...@psu.edu>, Efi Foufoula (UCI) <e...@uci.edu>
This year, HydroML 2025 will be hosted at UC Irvine and held at the UCLA Lake Arrowhead Lodge near Los Angeles. The exact meeting dates are being finalized but will be in the week of May 26 - 30 (likely May 27 - 29, with attendees leaving the morning of May 30). Attendees will stay at the facility in different chalets, and thus attendance will be restricted to a maximum of 100 - 120 people.
To finalize our reservation and plan effectively, we need to have an idea of how many people are interested. If you are interested in attending, please fill out the attendance interest form!
The following is a tentative abstract:
HydroML2025: Machine Learning in Water, Earth, and Climate Sciences
From the ancient water clocks of 4000 BC Egypt and Mesopotamia to the rise of personal computers, smartphones, and autonomous vehicles, automation has fundamentally impacted almost all aspects of human life. Today, Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing research and education across many scientific and engineering disciplines, among which are hydrology, hydrometeorology, climatology, and environmental engineering. Innovative methods and technologies juxtaposed with the quantum computing of the near future hold unprecedented potential for substantially advancing our understanding of subsurface, land-surface, boundary-layer, and atmospheric processes. This is key to enhancing predictions of climate change and extreme events—such as floods, droughts, fires, and hurricanes—and their impacts on infrastructure, human life, and ecosystem health, resilience, and sustainability. However, AI and ML also present many new challenges for Water, Earth, and Climate scientists. These include (but are not limited to):
- How to best adapt new methods and technologies to efficiently handle and analyze high-dimensional data sets from Earth-orbiting satellites, in-situ monitoring networks, and high-resolution physics-based models.
- How to leverage and synthesize the capabilities of data-driven and physics-informed ML, large language models (LLMs), and generative AI to enhance process understanding of water and energy cycles across spatiotemporal scales.
- How to train, constrain, test, and evaluate ML and differentiable models to maximize their physical realism and robustness while accurately portraying higher-order data patterns and signatures.
- How to balance deep learning model complexity, accuracy, and interpretability.
- How to embed AI technology and ML models within a probabilistic framework for real-time forecasting.
- How to quantify epistemic and prediction uncertainty of ML models and yield well-calibrated predictive distributions.
The HydroML 2025 symposium explores the use of AI and ML methods in Water, Earth and Climate research. This 3-day meeting aims to bring together Earth, environmental, and climate scientists and engineers with experts in statistics, optimization, and nonlinear control to explore the application and use of AI and ML methods in water and energy cycles. Specific areas of interest include uncertainty quantification, probabilistic (ensemble) forecasting, real-time prediction and decision making, and ML model malfunctioning and misspecification. Please see www.hydroml.org for more information and future updates, or email in...@hydroml.org with any questions.
Please feel free to forward this information to friends and colleagues, and we hope to see you in May!
Regards,
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