Fast and simple fluid simulator in Julia

Current CPU version of our solver for the Navier-Stokes equations

A differentiable PDE solving framework for machine learning

Python Dynamic Mode Decomposition

FLEXI: A high order discontinuous Galerkin framework for hyperbolic–parabolic conservation laws

GALÆXI: A high order discontinuous Galerkin framework for compressible flows on GPU systems

The materials for the Spring Mathematics in Materials course at the UTK MSE

An NCAR developed, parallelized and GPU-resident, large-eddy simulation code for accelerated modeling of the atmospheric boundary layer.

Lecture notes and code for the course PHYS6350 Computational Physics at the University of Houston

The materials for the Fall ML in Materials course at the UTK MSE

ModelFLOWs application

Physics Driven Deep Latent Variable Model

DNS data of flows over periodic hills with parameterized geometries, for data-driven turbulence modeling

A framework for simulating viscous incompressible flows about arbitrary body shapes.

Code accompanying my blog post: So, what is a physics-informed neural network?

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

Tools for solving partial differential equations by immersing surfaces and their operations in Cartesian grids

An Aircraft Design Toolbox

Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

The object-oriented HPC platform for CFD, plasma and multi-physics simulations whose development started in 2002 at the Von Karman Institute for Fluid Dynamics is finally open!