Jonghyun Lee
University of Hawaii at Manoa, HI·Earth & Environmental Science
Water moves constantly between land and sea, both above and below ground, and predicting that movement matters for drinking water, floods and shipping. Rivers carry water to the coast.
- Fields
Earth & Environmental Science
Engineering
Computer Science
- Topics
coastal groundwater
seawater intrusion
submarine groundwater discharge
- Grants
- 2 known grants, through Aug 2029
What they work on
- Build hybrid surrogate models from neural operators, networks that imitate expensive simulations, coupled through an open-source framework so groundwater–ocean exchange is simulated quickly while conserving mass at the land–sea boundary.
- Combine specialized neural networks in a mixture-of-experts design to capture coastal processes on very different timescales, from long-term tides and circulation to short storm surges.
- Use the differentiable framework for data assimilation and uncertainty estimates via generative AI priors and multi-fidelity Bayesian inference, testing on Hawaiʻi and Texas coastal benchmarks.
- Extend machine-learning methods for estimating river depth and discharge from SWOT satellite data to include river–groundwater exchange and improve discharge accuracy.
- Approximate the shallow water equations with physics-informed neural networks to estimate riverbed shape and flow in near real time, and package the methods in a public Python library.
More about the research
Underground, freshwater aquifers (layers of rock and soil that hold groundwater) trade water with the ocean. Measuring these flows directly is slow and expensive, and detailed computer simulations are too costly to run in real time. The lab's two projects pair physics-based models with machine learning. One builds fast forecasts of coastal groundwater–ocean exchange. The other estimates river depth and flow from satellite data.
Why it matters
Coastal communities face saltwater contamination of their drinking-water aquifers, along with storm surges and rising seas. Fast forecasts of seawater intrusion and groundwater discharge could help water managers and planners in places like Hawaiʻi and Texas make better decisions about water supply and infrastructure. Satellite-based estimates of river depth and flow could support flood-risk management, water planning and safe navigation without costly field surveys.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Collaborative Research: CAIG: Improving the understanding of coastal groundwater and ocean exchange using machine learning
NSF · Sep 2026 – Aug 2029
RII Track-4:@NASA: Combining Physics and Deep Learning for Accurate River Discharge and Bathymetry Estimation from the Surface Water and Ocean Topography Mission
NSF · Nov 2023 – Oct 2027
Closest labs
Sri Hari Krishn Narayanan
University of Chicago, IL·Computer Science
Develop an open-source, general-purpose automatic differentiation framework in Julia that computes exact derivatives of program code for both large-scale Earth system models and scientific machine learning methods.
Patrick Heimbach
University of Texas at Austin, TX·Earth & Environmental Science
Set deep-ocean observing requirements by working with Global Ocean Observing System expert panels to refine Essential Ocean Variables, and apply them through regional demonstration projects.
Xin Li
Texas A&M University-College Station, TX·Engineering
Develop physics-informed neural networks, AI models constrained by wave mechanics, that estimate nearshore bathymetry, waves and currents simultaneously from optical and lidar images of the surf zone.
The school
University of Hawaii at Manoa: 23 labs on PI Pages, $376M a year on research, 182 research doctorates a year.
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