JRice15/physics-informed-autoencoders
Code for Rice et al. 2020 "Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forceasting"
This project helps oceanographers and climate scientists predict long-term sea-surface temperatures using satellite data, and fluid dynamics researchers analyze ideal fluid flow. It takes historical sea-surface temperature data or simulated fluid dynamics data and produces forecasts of future conditions. The primary users are researchers focused on environmental modeling and predictive analysis in earth sciences.
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Use this if you are a researcher developing or applying physics-informed machine learning models for forecasting complex spatiotemporal environmental data like sea-surface temperatures.
Not ideal if you need a plug-and-play forecasting tool for general time-series data or if you are not comfortable with command-line interfaces and deep learning model training.
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Language
Python
License
GPL-3.0
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Last pushed
Sep 09, 2025
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