Home for hydrology models and augmentations to be used with differential modeling package dMG.

GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations - Source Code

Awesome Discrete Global Grid Systems (DGGS)

A Fair and Scalable Time Series Forecasting Benchmark and Toolkit.

Hydra is a framework for elegantly configuring complex applications

Open-source, cloud-native transactional tensor storage engine

Basic Model Interface for Python

18.335 - Introduction to Numerical Methods course

Interactively inspect module inputs, outputs, parameters, and gradients.

PyTorch based Probabilistic Time Series forecasting framework based on GluonTS backend

Research Software for Neural Weather Prediction for Limited Area Modeling

Repo to the paper "Message Passing Neural PDE Solvers"

Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics (NeurIPS '22)

JAX - A curated list of resources https://github.com/google/jax

Track emissions from Compute and recommend ways to reduce their impact on the environment.

Efficient transducers for Julia

A framework for out-of-core and parallel execution

Python framework for short-term ensemble prediction systems.

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.

🟠 A study guide to learn about Graph Neural Networks (GNNs)

High-Performance Symbolic Regression in Python and Julia

Package for extracting and mapping the results of every single tensor operation in a PyTorch model in one line of code.

A survey and paper list of current Diffusion Model for Time Series and SpatioTemporal Data with awesome resources (paper, application, review, survey, etc.).

A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods

Intuitive scientific computing with dimension types for Jax, PyTorch, TensorFlow & NumPy

Exercise solutions and explanations for the book Probability Theory: The Logic of Science by E.T. Jaynes. Created by the reading group at r/jaynesprobability

Elucidating the Design Space of Diffusion-Based Generative Models (EDM)

A differentiable PDE solving framework for machine learning