Writing a differentiable fluid solver in Julia
October 6, 2024
Research project
A differentiable fluid solver in Julia
IncompressibleNavierStokes.jl solves the incompressible Navier–Stokes equations with hardware-agnostic kernels compiled from a single Julia source, and every discrete operator has a hand-written adjoint — so a neural closure model can be trained through the solver while embedded in a running LES. Supports double-precision DNS up to 840³ on a single GPU.
Paper
Interactive explainer