Patrick Heimbach
University of Texas at Austin, TX·Earth & Environmental Science
The ocean below 200 meters is one of the least observed parts of the planet. Yet it helps regulate climate, holds vast biodiversity, and contains mineral and energy resources.
- Fields
Earth & Environmental Science
Computer Science
Mathematics & Statistics
- Topics
deep ocean
ocean observing
Essential Ocean Variables
- Grants
- 2 known grants, through Jul 2027
What they work on
- 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.
- Promote findable, accessible, interoperable, and reusable (FAIR) deep-sea data practices and train a network of Deep Ocean Early career Researchers in observing, modeling, and data science.
- Develop a general-purpose automatic differentiation framework in Julia that supplies model derivatives so Earth system models can be combined with scientific machine learning for Bayesian inversion.
- Apply automatic differentiation-enabled ocean circulation and ice flow models to parameter and state estimation, data assimilation, uncertainty quantification, and observing system design.
More about the research
Studying the deep ocean and the wider Earth system requires two things: coordinated observations, and computer models that can learn from sparse data. This work connects both. One project organizes how the world observes the deep ocean. The other builds software tools that help climate models learn from observations.
Why it matters
Better coordinated deep-ocean observations and FAIR data could give policymakers and regulators the evidence they need for decisions about climate, biodiversity, and deep-sea resources. Open, differentiable ocean and ice models could improve climate projections and their uncertainty estimates. According to the grant, partners are also applying the same tools to power grid design, personalized drug dosing, and EEG design.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Collaborative Research: Frameworks: Convergence of Bayesian inverse methods and scientific machine learning in Earth system models through universal differentiable programming
NSF · Aug 2021 – Jul 2027
AccelNet-Implementation: Implementing a Deep Ocean Observing Strategy (iDOOS)
NSF · Oct 2021 – Sep 2026
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.
Jonghyun Lee
University of Hawaii at Manoa, HI·Earth & Environmental Science
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.
Alan Edelman
Massachusetts Institute of Technology, MA·Computer Science
Build an equation-based geoscientific modeling system in which a compiler automatically turns systems of equations into computer models, adding components prioritized at community workshops and documenting them on a dedicated website.
The school
University of Texas at Austin: 49 labs on PI Pages, $1.1B a year on research, 871 research doctorates a year.
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