Open-source deep-learning framework for exploring, building and deploying AI weather/climate workflows.

Massively parallel training of machine-learning based weather and climate models

A PyTorch based deep-learning toolkit for developing DL models for physical systems

A package to download ECMWF open data

Repo of abstracted workflow comparable to existing numerical solvers for training data-free and data-driven AI surrogates

Earth-2 Model Intercomparison Project (MIP) is a validation framework that enables scientists to evaluate the skill of AI models for weather and climate.

Repo of optimized training recipes for accelerating PyTorch workflows of AI driven surrogates for physical systems

A native Go implementation of NetCDF4.

Create and execute VTK pipelines in Blender Node Editor

Uncertainty Quantification of RANS Data-Driven Turbulence Modeling

Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

A modern LaTeX Beamer theme