neuralgcm/neuralgcm
Hybrid ML + physics model of the Earth's atmosphere
What it solves
NeuralGCM addresses the challenge of creating accurate weather and climate simulations by combining the strengths of traditional physics-based models with the flexibility of machine learning.
How it works
It is a Python library that enables the construction of hybrid atmospheric models. These models integrate machine learning components with traditional physics-based General Circulation Models (GCMs) to simulate weather and climate patterns.
Who it’s for
This tool is designed for researchers and developers working in atmospheric science, meteorology, and climate modeling who want to leverage ML to improve simulation accuracy.
Highlights
- Hybrid approach combining ML and physics
- Dedicated Python library for atmospheric modeling
- Open-source code under Apache 2.0 license
- Publicly available trained model weights
Related
- Project
- Project
- Project
- Project