Building a Machine-Learning Climate Emulator
E-PSCI 226
Subject & Catalog Number
Course Information
Description
In this project-based graduate course, students will build a machine-learning emulator of El Niño events by training on climate model simulations and gain experience lecturing about machine learning. ML emulators can learn from observations or complex models and then simulate or predict a given system with exceptional efficiency. The introductory lectures will introduce basic ENSO dynamics and some machine learning ideas, including supervised learning, neural networks, backpropagation, overfitting, validation, etc. Each student team will then attempt a different approach to building the emulator and will provide a few lectures to the class on its approach. These approaches will include convolutional encoder-decoder networks (U-Net), transformers, Fourier neural operators, and graph neural networks, all introduced as both deterministic and stochastic/generative emulation methods. Students are encouraged to also apply the emulation methods to their own research as an optional class assignment.
Recommended Prep
Python programming experience. Prior machine-learning coursework is not expected.
Available for Harvard Cross Registration
