Differentiable Programming for Oceanography with Patrick Heimbach
EPISODE 557
|
JANUARY
31,
2022
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About this Episode
Today we’re joined by Patrick Heimbach, a professor at the University of Texas working at the intersection of ML and oceanography. In our conversation with Patrick, we explore some of the challenges of computational oceanography, the potential use cases for machine learning in this field, as well as how it can be used to support scientists in solving simulation problems, and the role of differential programming, and how it is expressed in his work.
About the Guest
Patrick Heimbach
University of Texas
Resources
- Differentiable programming in Julia for Earth system modeling (DJ4Earth)
- NASA ECCO
- Paper: Practical global oceanic state estimatio
- Paper: Greenland ice-sheet volume sensitivity to basal, surface and initial conditions derived from an adjoint model
- Paper: Composing Modeling and Simulation with Machine Learning in Julia
- Paper: Reverse-mode automatic differentiation and optimization of GPU kernels via enzyme
