Popular GitHub Repositories for Geophysical Fluid

A differentiable PDE solving framework for machine learning

PDEBench: An Extensive Benchmark for Scientific Machine Learning

Computational Fluid Dynamics in JAX

Differentiable Fluid Dynamics Package

A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks

Exascale simulation of multiphase/physics fluid dynamics

A large-scale benchmark for machine learning methods in fluid dynamics

Code repository for "Lagrangian Fluid Simulation with Continuous Convolutions", ICLR 2020.

This is an on-CPU real-time conversational system for two-way speech communication with AI models, utilizing a continuous streaming architecture for fluid conversations with immediate responses and natural interruption handling.

Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube problems and plane stress linear elasticity boundary value problems

A pytorch implementaion of physics informed neural networks for two dimensional NS equation

compressing physics with neural networks

Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.

Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."

Official implementation of Diffusion Graph Networks (DGNs)

Generative Adversarial Network (GAN) for physically realistic enrichment of turbulent flow fields

Fluid, elastic data abstraction and acceleration for BigData/AI applications in cloud. (Project under CNCF)

[CVPR 2025 Oral] FluidNexus: 3D Fluid Reconstruction and Prediction from a Single Video

Python Scripts and Jupyter Notebooks to Prepare Geophysical Figures and Geographic Maps via PyGMT (Pythonic Interface for GMT)

High order and sparse layers in pytorch. Lagrange Polynomial, Piecewise Lagrange Polynomial, Piecewise Discontinuous Lagrange Polynomial (Chebyshev nodes) and Fourier Series layers of arbitrary order. Piecewise implementations could be thought of as a 1d grid (for each neuron) where each grid element is Lagrange polynomial. Both full connected and convolutional layers included.